Phylogenetic analysis of gene expression

MAFin: motif detection in multiple alignment files

MOTIVATION

Whole Genome and Proteome Alignments, represented by the multiple alignment file format, have become a standard approach in comparative genomics and proteomics. These often require identifying conserved motifs, which is crucial for understanding functional and evolutionary relationships. However, current approaches lack a direct method for motif detection within MAF files. We present MAFin, a novel tool that enables efficient motif detection and conservation analysis in MAF files to address this gap, streamlining genomic and proteomic research.

RESULTS

We developed MAFin, the first motif detection tool for Multiple Alignment Format files. MAFin enables the multithreaded search of conserved motifs using three approaches: (i) using user-specified k-mers to search the sequences. (ii) with regular expressions, in which case one or more patterns are searched, and (iii) with predefined Position Weight Matrices. Once the motif has been found, MAFin detects the motif instances and calculates the conservation across the aligned sequences. MAFin also calculates a conservation percentage, which provides information about the conservation levels of each motif across the aligned sequences, based on the number of matches relative to the length of the motif. A set of statistics enables the interpretation of each motif's conservation level, and the detected motifs are exported in JSON and CSV files for downstream analyses.

AVAILABILITY AND IMPLEMENTATION

MAFin is offered as a Python package under the GPL license as a multi-platform application and is available at: https://github.com/Georgakopoulos-Soares-lab/MAFin.

Leveraging graphical model techniques to study evolution on phylogenetic networks

The evolution of molecular and phenotypic traits is commonly modelled using Markov processes along a phylogeny. This phylogeny can be a tree, or a network if it includes reticulations, representing events such as hybridization or admixture. Computing the likelihood of data observed at the leaves is costly as the size and complexity of the phylogeny grows. Efficient algorithms exist for trees, but cannot be applied to networks. We show that a vast array of models for trait evolution along phylogenetic networks can be reformulated as graphical models, for which efficient belief propagation algorithms exist. We provide a brief review of belief propagation on general graphical models, then focus on linear Gaussian models for continuous traits. We show how belief propagation techniques can be applied for exact or approximate (but more scalable) likelihood and gradient calculations, and prove novel results for efficient parameter inference of some models. We highlight the possible fruitful interactions between graphical models and phylogenetic methods. For example, approximate likelihood approaches have the potential to greatly reduce computational costs for phylogenies with reticulations.This article is part of the theme issue '"A mathematical theory of evolution": phylogenetic models dating back 100 years'.

Transcriptomic Data Reveal Divergent Paths of Chitinase Evolution Underlying Dietary Convergence in Anteaters and Pangolins

Ant-eating mammals represent a textbook example of convergent evolution. Among them, anteaters and pangolins exhibit the most extreme convergent phenotypes with complete tooth loss, elongated skulls, protruding tongues, and hypertrophied salivary glands producing large amounts of saliva. However, comparative genomic analyses have shown that anteaters and pangolins differ in their chitinase acidic gene (CHIA) repertoires, which potentially degrade the chitinous exoskeletons of ingested ants and termites. While the southern tamandua (Tamandua tetradactyla) harbors four functional CHIA paralogs (CHIA1-4), Asian pangolins (Manis spp.) have only one functional paralog (CHIA5). Here, we performed a comparative transcriptomic analysis of salivary glands in 33 placental species, including 16 novel transcriptomes from ant-eating species and close relatives. Our results suggest that salivary glands play an important role in adaptation to an insect-based diet, as expression of different CHIA paralogs is observed in insectivorous species. Furthermore, convergently evolved pangolins and anteaters express different chitinases in their digestive tracts. In the Malayan pangolin, CHIA5 is overexpressed in all major digestive organs, whereas in the southern tamandua, all four functional paralogs are expressed, at very high levels for CHIA1 and CHIA2 in the pancreas and for CHIA3 and CHIA4 in the salivary glands, stomach, liver, and pancreas. Overall, our results demonstrate that divergent molecular mechanisms within the chitinase acidic gene family underlie convergent adaptation to the ant-eating diet in pangolins and anteaters. This study highlights the role of historical contingency and molecular tinkering of the chitin digestive enzyme toolkit in this classic example of convergent evolution.

MAFin: Motif Detection in Multiple Alignment Files

Motivation

Genome and Proteome Alignments, represented by the Multiple Alignment File (MAF) format, have become a standard approach in the field of comparative genomics and proteomics. However, current approaches lack a direct method for motif detection within MAF files. To address this gap, we present MAFin, a novel tool that enables efficient motif detection and conservation analysis in MAF files, streamlining genomic and proteomic research.

Results

We developed MAFin, the first motif detection tool for Multiple Alignment Format files. MAFin enables the multithreaded search of conserved motifs using three approaches: 1) by using user-specified k-mers to search the sequences. 2) with regular expressions, in which case one or more patterns are searched, and 3) with predefined Position Weight Matrices. Once the motif has been found, MAFin detects the motif instances and calculates the conservation across the aligned sequences. MAFin also calculates a conservation percentage, which provides information about the conservation levels of each motif across the aligned sequences, based on the number of matches relative to the length of the motif. A set of statistics enable the interpretation of each motif's conservation level, and the detected motifs are exported in JSON and CSV files for downstream analyses.

Availability

MAFin is released as a Python package under the GPL license as a multi-platform application and is available at: https://github.com/Georgakopoulos-Soares-lab/MAFin.

Relevance of the regulation of the brain-placental axis to the nocturnal bottleneck of mammals

INTRODUCTION

Evolutionary theory suggests that the ancestors of all placental animals were nocturnal. Visual perceptive function of mammalian brain has evolved extensively, but nearly 70 % of today's mammals are still nocturnal. While placental influence on brain development is known, if placenta plays a role in the visual perceptive function of mammalian brain remains untested. The present study aims to test this hypothesis.

METHODS

In this study, single-nuclei RNA sequencing was performed to identify genes expressed in the pig placenta and fetal brain, and then compared with the orthologous genes expressed in the placenta and fetal brain cells of mouse. Differential gene expression analysis was performed to identify placental genes regulated differentially between nocturnal and diurnal animals. Phylogenetic modeling was performed to test correlated evolution between placenta type, and the nocturnal or diurnal activity among different mammals.

RESULTS

The results showed that genes differentially regulated in the fetal brain were related to visual perception whereas the placental genes were related to the nocturnal or diurnal activity in placental animals. Phylogenetic modeling of these genes in thirty-four diverse mammalian species showed evidence for evolutionary link between placenta and the nocturnal/diurnal activity in animals.

DISCUSSION

The findings of this study suggest that the placenta plays a role in the evolution of visual perceptive function of brain to shape the nocturnal or diurnal activity of placental animals.

Comparative Analysis of Maternal Gene Expression Patterns Unravels Evolutionary Signatures Across Reproductive Modes

Maternal genes have a pivotal role in regulating metazoan early development. As such their functions have been extensively studied since the dawn of developmental biology. The temporal and spatial dynamics of their transcripts have been thoroughly described in model organisms and their functions have been undergoing heavy investigations. Yet, less is known about the evolutionary changes shaping their presence within diverse oocytes. Due to their unique maternal inheritance pattern, a high degree is predicted to be present when it comes to their expression. Insofar only limited and conflicting results have emerged around it. Here, we set out to elucidate which evolutionary changes could be detected in the maternal gene expression patterns using phylogenetic comparative methods on RNAseq data from 43 species. Using normalized gene expression values and fold change information throughout early development we set out to find the best-fitting evolutionary model. Through modeling, we find evidence supporting both the high degree of divergence and constraint on gene expression values, together with their temporal dynamics. Furthermore, we find that maternal gene expression alone can be used to explain the reproductive modes of different species. Together, these results suggest a highly dynamic evolutionary landscape of maternal gene expression. We also propose a possible functional dichotomy of maternal genes which is influenced by the reproductive strategy undertaken by examined species.

Integrating phylogenies into single-cell RNA sequencing analysis allows comparisons across species, genes, and cells

Comparisons of single-cell RNA sequencing (scRNA-seq) data across species can reveal links between cellular gene expression and the evolution of cell functions, features, and phenotypes. These comparisons evoke evolutionary histories, as depicted by phylogenetic trees, that define relationships between species, genes, and cells. This Essay considers each of these in turn, laying out challenges and solutions derived from a phylogenetic comparative approach and relating these solutions to previously proposed methods for the pairwise alignment of cellular dimensional maps. This Essay contends that species trees, gene trees, cell phylogenies, and cell lineages can all be reconciled as descriptions of the same concept-the tree of cellular life. By integrating phylogenetic approaches into scRNA-seq analyses, challenges for building informed comparisons across species can be overcome, and hypotheses about gene and cell evolution can be robustly tested.

Evaluating the Performance of Widely Used Phylogenetic Models for Gene Expression Evolution

Phylogenetic comparative methods are increasingly used to test hypotheses about the evolutionary processes that drive divergence in gene expression among species. However, it is unknown whether the distributional assumptions of phylogenetic models designed for quantitative phenotypic traits are realistic for expression data and importantly, the reliability of conclusions of phylogenetic comparative studies of gene expression may depend on whether the data is well described by the chosen model. To evaluate this, we first fit several phylogenetic models of trait evolution to 8 previously published comparative expression datasets, comprising a total of 54,774 genes with 145,927 unique gene-tissue combinations. Using a previously developed approach, we then assessed how well the best model of the set described the data in an absolute (not just relative) sense. First, we find that Ornstein-Uhlenbeck models, in which expression values are constrained around an optimum, were the preferred models for 66% of gene-tissue combinations. Second, we find that for 61% of gene-tissue combinations, the best-fit model of the set was found to perform well; the rest were found to be performing poorly by at least one of the test statistics we examined. Third, we find that when simple models do not perform well, this appears to be typically a consequence of failing to fully account for heterogeneity in the rate of the evolution. We advocate that assessment of model performance should become a routine component of phylogenetic comparative expression studies; doing so can improve the reliability of inferences and inspire the development of novel models.

Evaluating the Performance of Widely Used Phylogenetic Models for Gene Expression Evolution

Phylogenetic comparative methods are increasingly used to test hypotheses about the evolutionary processes that drive divergence in gene expression among species. However, it is unknown whether the distributional assumptions of phylogenetic models designed for quantitative phenotypic traits are realistic for expression data and importantly, the reliability of conclusions of phylogenetic comparative studies of gene expression may depend on whether the data is well-described by the chosen model. To evaluate this, we first fit several phylogenetic models of trait evolution to 8 previously published comparative expression datasets, comprising a total of 54,774 genes with 145,927 unique gene-tissue combinations. Using a previously developed approach, we then assessed how well the best model of the set described the data in an absolute (not just relative) sense. First, we find that Ornstein-Uhlenbeck models, in which expression values are constrained around an optimum, were the preferred model for 66% of gene-tissue combinations. Second, we find that for 61% of gene-tissue combinations, the best fit model of the set was found to perform well; the rest were found to be performing poorly by at least one of the test statistics we examined. Third, we find that when simple models do not perform well, this appears to be typically a consequence of failing to fully account for heterogeneity in the rate of the evolution. We advocate that assessment of model performance should become a routine component of phylogenetic comparative expression studies; doing so can improve the reliability of inferences and inspire the development of novel models.

Knocking down the expression of the molecular motors, myosin A, C and F genes in Toxoplasma gondii to decrease the parasite virulence

Toxoplasmosis is a serious parasitic infection and novel therapeutic options are highly demanded to effectively eliminate it. In current study, Toxoplasma gondii myosin A, C and F genes were knocked down using small interference RNA (siRNA) method and the parasite survival and virulence was evaluated in vitro and in vivo. The parasites were transfected with specific siRNA, virtually designed for myosin mRNAs, and co-cultured with human foreskin fibroblasts. The transfection rate and the viability of the transfected parasites were measured using flow cytometry and methyl thiazole tetrazolium (MTT) assays, respectively. Finally, the survival of BALB/c mice infected with siRNAs-transfected T. gondii was assessed. It was demonstrated that a transfection rate of 75.4% existed for siRNAs, resulting in 70% (P = 0.032), 80.6% (P = 0.017) and 85.5% (P = 0.013) gene suppression for myosin A, C and F in affected parasites, respectively, which was subsequently confirmed by Western blot analysis. Moreover, lower parasite viability was observed in those with knocked down myosin C with 80% (P = 0.0001), followed by 86.15% (P = 0.004) for myosin F and 92.3% (P = 0.083) for myosin A. Considerably higher mouse survival (about 40 h) was, also, demonstrated in mice challenged with myosin siRNA-transfected T. gondii, in comparison with control group challenged with wild-type parasites. In conclusion, myosin proteins knock down proposes a promising therapeutic strategy to combat toxoplasmosis.

CAGEE: Computational Analysis of Gene Expression Evolution

Despite the increasing abundance of whole transcriptome data, few methods are available to analyze global gene expression across phylogenies. Here, we present a new software package (Computational Analysis of Gene Expression Evolution [CAGEE]) for inferring patterns of increases and decreases in gene expression across a phylogenetic tree, as well as the rate at which these changes occur. In contrast to previous methods that treat each gene independently, CAGEE can calculate genome-wide rates of gene expression, along with ancestral states for each gene. The statistical approach developed here makes it possible to infer lineage-specific shifts in rates of evolution across the genome, in addition to possible differences in rates among multiple tissues sampled from the same species. We demonstrate the accuracy and robustness of our method on simulated data and apply it to a data set of ovule gene expression collected from multiple self-compatible and self-incompatible species in the genus Solanum to test hypotheses about the evolutionary forces acting during mating system shifts. These comparisons allow us to highlight the power of CAGEE, demonstrating its utility for use in any empirical system and for the analysis of most morphological traits. Our software is available at https://github.com/hahnlab/CAGEE/.

An Effective Integrated Machine Learning Framework for Identifying Severity of Tomato Yellow Leaf Curl Virus and Their Experimental Validation

Tomato yellow leaf curl virus (TYLCV) dispersed across different countries, specifically to subtropical regions, associated with more severe symptoms. Since TYLCV was first isolated in 1931, it has been a menace to tomato industrial production worldwide over the past century. Three groups were newly isolated from TYLCV-resistant tomatoes in 2022; however, their functions are unknown. The development of machine learning (ML)-based models using characterized sequences and evaluating blind predictions is one of the major challenges in interdisciplinary research. The purpose of this study was to develop an integrated computational framework for the accurate identification of symptoms (mild or severe) based on TYLCV sequences (isolated in Korea). For the development of the framework, we first extracted 11 different feature encodings and hybrid features from the training data and then explored 8 different classifiers and developed their respective prediction models by using randomized 10-fold cross-validation. Subsequently, we carried out a systematic evaluation of these 96 developed models and selected the top 90 models, whose predicted class labels were combined and considered as reduced features. On the basis of these features, a multilayer perceptron was applied and developed the final prediction model (IML-TYLCVs). We conducted blind prediction on 3 groups using IML-TYLCVs, and the results indicated that 2 groups were severe and 1 group was mild. Furthermore, we confirmed the prediction with virus-challenging experiments of tomato plant phenotypes using infectious clones from 3 groups. Plant virologists and plant breeding professionals can access the user-friendly online IML-TYLCVs web server at https://balalab-skku.org/IML-TYLCVs, which can guide them in developing new protection strategies for newly emerging viruses.

A Phylogenetic Framework to Simulate Synthetic Interspecies RNA-Seq Data

Interspecies RNA-Seq datasets are increasingly common, and have the potential to answer new questions about the evolution of gene expression. Single-species differential expression analysis is now a well-studied problem that benefits from sound statistical methods. Extensive reviews on biological or synthetic datasets have provided the community with a clear picture on the relative performances of the available methods in various settings. However, synthetic dataset simulation tools are still missing in the interspecies gene expression context. In this work, we develop and implement a new simulation framework. This tool builds on both the RNA-Seq and the phylogenetic comparative methods literatures to generate realistic count datasets, while taking into account the phylogenetic relationships between the samples. We illustrate the usefulness of this new framework through a targeted simulation study, that reproduces the features of a recently published dataset, containing gene expression data in adult eye tissue across blind and sighted freshwater crayfish species. Using our simulated datasets, we perform a fair comparison of several approaches used for differential expression analysis. This benchmark reveals some of the strengths and weaknesses of both the classical and phylogenetic approaches for interspecies differential expression analysis, and allows for a reanalysis of the crayfish dataset. The tool has been integrated in the R package compcodeR, freely available on Bioconductor.

The evolution of ovary-biased gene expression in Hawaiian Drosophila

With detailed data on gene expression accessible from an increasingly broad array of species, we can test the extent to which our developmental genetic knowledge from model organisms predicts expression patterns and variation across species. But to know when differences in gene expression across species are significant, we first need to know how much evolutionary variation in gene expression we expect to observe. Here we provide an answer by analyzing RNAseq data across twelve species of Hawaiian Drosophilidae flies, focusing on gene expression differences between the ovary and other tissues. We show that over evolutionary time, there exists a cohort of ovary specific genes that is stable and that largely corresponds to described expression patterns from laboratory model Drosophila species. Our results also provide a demonstration of the prediction that, as phylogenetic distance increases, variation between species overwhelms variation between tissue types. Using ancestral state reconstruction of expression, we describe the distribution of evolutionary changes in tissue-biased expression, and use this to identify gains and losses of ovary-biased expression across these twelve species. We then use this distribution to calculate the evolutionary correlation in expression changes between genes, and demonstrate that genes with known interactions in D. melanogaster are significantly more correlated in their evolution than genes with no or unknown interactions. Finally, we use this correlation matrix to infer new networks of genes that share evolutionary trajectories, and we present these results as a dataset of new testable hypotheses about genetic roles and interactions in the function and evolution of the Drosophila ovary.

Family dinner: Transcriptional plasticity of five Noctuidae (Lepidoptera) feeding on three host plant species

Polyphagous insects often show specialization in feeding on different host plants in terms of survival and growth and, therefore, can be considered minor or major pests of particular hosts. Whether polyphagous insects employ a common transcriptional response to cope with defenses from diverse host plants is under-studied. We focused on patterns of transcriptional plasticity in polyphagous moths (Noctuidae), of which many species are notorious pests, in relation to herbivore performance on different host plants. We compared the transcriptional plasticity of five polyphagous moth species feeding and developing on three different host plant species. Using a comparative phylogenetic framework, we evaluated if successful herbivory, as measured by larval performance, is determined by a shared or lineage-specific transcriptional response. The upregulated transcriptional activity, or gene expression pattern, of larvae feeding on the different host plants and artificial control diet was highly plastic and moth species-specific. Specialization, defined as high herbivore success for specific host plants, was not generally linked to a lower number of induced genes. Moths that were more distantly related and showing high herbivore success for certain host plants showed shared expression of multiple homologous genes, indicating convergence. We further observed specific transcriptional responses within phylogenetic lineages. These expression patterns for specific host plant species are likely caused by shared evolutionary histories, for example, symplesiomorphic patterns, and could therefore not be associated with herbivore success alone. Multiple gene families, with roles in plant digestion and detoxification, were widely expressed in response to host plant feeding but again showed highly moth species-specific. Consequently, high herbivore success for specific host plants is also driven by species-specific transcriptional plasticity. Thus, potential pest moths display a complex and species-specific transcriptional plasticity.

Layered evolution of gene expression in "superfast" muscles for courtship

The process by which new complex traits evolve has been a persistent conundrum throughout the history of evolutionary inquiry. How multiple physiological changes at the organism level and genetic changes at the molecular level combine is still unclear for many traits. Here, we studied the displays of manakins, who beat their wings together at nearly twice the speed of other songbirds to produce a loud “snap” that attracts mates. We simultaneously analyzed evolution of gene expression levels and gene sequences to identify key genes related to muscle contractions and tissue regeneration after stress. Our results show how innovative behavioral traits evolve as a layered process where recent molecular shifts build on ancestral genetic evolutionary changes.

Identifying the molecular process of complex trait evolution is a core goal of biology. However, pinpointing the specific context and timing of trait-associated changes within the molecular evolutionary history of an organism remains an elusive goal. We study this topic by exploring the molecular basis of elaborate courtship evolution, which represents an extraordinary example of trait innovation. Within the behaviorally diverse radiation of Central and South American manakin birds, species from two separate lineages beat their wings together using specialized “superfast” muscles to generate a “snap” that helps attract mates. Here, we develop an empirical approach to analyze phylogenetic lineage-specific shifts in gene expression in the key snap-performing muscle and then integrate these findings with comparative transcriptomic sequence analysis. We find that rapid wing displays are associated with changes to a wide range of molecular processes that underlie extreme muscle performance, including changes to calcium trafficking, myocyte homeostasis and metabolism, and hormone action. We furthermore show that these changes occur gradually in a layered manner across the species history, wherein which ancestral genetic changes to many of these molecular systems are built upon by later species-specific shifts that likely finalized the process of display performance adaptation. Our study demonstrates the potential for combining phylogenetic modeling of tissue-specific gene expression shifts with phylogenetic analysis of lineage-specific sequence changes to reveal holistic evolutionary histories of complex traits.

Evolution of gene expression across species and specialized zooids in Siphonophora

Siphonophores are complex colonial animals, consisting of asexually produced bodies (zooids) that are functionally specialized for specific tasks, including feeding, swimming, and sexual reproduction. Though this extreme functional specialization has captivated biologists for generations, its genomic underpinnings remain unknown. We use RNA-seq to investigate gene expression patterns in five zooids and one specialized tissue across seven siphonophore species. Analyses of gene expression across species present several challenges, including identification of comparable expression changes on gene trees with complex histories of speciation, duplication, and loss. We examine gene expression within species, conduct classical analyses examining expression patterns between species, and introduce species branch filtering, which allows us to examine the evolution of expression across species in a phylogenetic framework. Within and across species, we identified hundreds of zooid-specific and species-specific genes, as well as a number of putative transcription factors showing differential expression in particular zooids and developmental stages. We found that gene expression patterns tended to be largely consistent in zooids with the same function across species, but also some large lineage-specific shifts in gene expression. Our findings show that patterns of gene expression have the potential to define zooids in colonial organisms. Traditional analyses of the evolution of gene expression focus on the tips of gene phylogenies, identifying large-scale expression patterns that are zooid or species variable. The new explicit phylogenetic approach we propose here focuses on branches (not tips) offering a deeper evolutionary perspective into specific changes in gene expression within zooids along all branches of the gene (and species) trees.

Sensitive protein alignments at tree-of-life scale using DIAMOND

We are at the beginning of a genomic revolution in which all known species are planned to be sequenced. Accessing such data for comparative analyses is crucial in this new age of data-driven biology. Here, we introduce an improved version of DIAMOND that greatly exceeds previous search performances and harnesses supercomputing to perform tree-of-life scale protein alignments in hours, while matching the sensitivity of the gold standard BLASTP.

An updated version of DIAMOND uses improved algorithmic procedures and a customized high-performance computing framework to make seemingly prohibitive large-scale protein sequence alignments feasible.

The Genomic Impact of Mycoheterotrophy in Orchids

Mycoheterotrophic plants have lost the ability to photosynthesize and obtain essential mineral and organic nutrients from associated soil fungi. Despite involving radical changes in life history traits and ecological requirements, the transition from autotrophy to mycoheterotrophy has occurred independently in many major lineages of land plants, most frequently in Orchidaceae. Yet the molecular mechanisms underlying this shift are still poorly understood. A comparison of the transcriptomes of Epipogium aphyllum and Neottia nidus-avis, two completely mycoheterotrophic orchids, to other autotrophic and mycoheterotrophic orchids showed the unexpected retention of several genes associated with photosynthetic activities. In addition to these selected retentions, the analysis of their expression profiles showed that many orthologs had inverted underground/aboveground expression ratios compared to autotrophic species. Fatty acid and amino acid biosynthesis as well as primary cell wall metabolism were among the pathways most impacted by this expression reprogramming. Our study suggests that the shift in nutritional mode from autotrophy to mycoheterotrophy remodeled the architecture of the plant metabolism but was associated primarily with function losses rather than metabolic innovations.

The strength and form of natural selection on transcript abundance in the wild

Gene transcription variation is known to contribute to disease susceptibility and adaptation, but we currently know very little about how contemporary natural selection shapes transcript abundance. Here, we propose a novel analytical framework to quantify the strength and form of ongoing natural selection at the transcriptome level in a wild vertebrate. We estimated selection on transcript abundance in a cohort of a wild salmonid fish (Salmo trutta) affected by an extracellular myxozoan parasite (Tetracapsuloides bryosalmonae) through mark–recapture field sampling and the integration of RNA‐sequencing with classical regression‐based selection analysis. We show, based on fin transcriptomes of the host, that infection by the parasite and subsequent host survival is linked to upregulation of mitotic cell cycle process. We also detect a widespread signal of disruptive selection on transcripts linked to host immune defence, host–pathogen interactions, cellular repair and maintenance. Our results provide insights into how selection can be measured at the transcriptome level to dissect the molecular mechanisms of contemporary evolution driven by climate change and emerging anthropogenic threats. We anticipate that the approach described here will enable critical information on the molecular processes and targets of natural selection to be obtained in real time.

see also the Perspective by Matthew P. Josephson and James K. Bull.

Adaptation of plasticity to projected maximum temperatures and across climatically defined bioregions

Resilience to environmental stressors due to climate warming is influenced by local adaptations, including plastic responses. The recent literature has focused on genomic signatures of climatic adaptation, but little is known about how plastic capacity may be influenced by biogeographic and evolutionary processes. We investigate phenotypic plasticity as a target of climatic selection, hypothesizing that lineages that evolved in warmer climates will exhibit greater plastic adaptive resilience to upper thermal stress. This was experimentally tested by comparing transcriptomic responses within and among temperate, subtropical, and desert ecotypes of Australian rainbowfish subjected to contemporary and projected summer temperatures. Critical thermal maxima were estimated, and ecological niches delineated using bioclimatic modeling. A comparative phylogenetic expression variance and evolution model was used to assess plastic and evolved changes in gene expression. Although 82% of all expressed genes were found in the three ecotypes, they shared expression patterns in only 5 out of 236 genes that responded to the climate change experiment. A total of 532 genes showed signals of adaptive (i.e., genetic-based) plasticity due to ecotype-specific directional selection, and 23 of those responded to projected summer temperatures. Network analyses demonstrated centrality of these genes in thermal response pathways. The greatest adaptive resilience to upper thermal stress was shown by the subtropical ecotype, followed by the desert and temperate ecotypes. Our findings indicate that vulnerability to climate change will be highly influenced by biogeographic factors, emphasizing the value of integrative assessments of climatic adaptive traits for accurate estimation of population and ecosystem responses.

Gene expression of functionally-related genes coevolves across fungal species: detecting coevolution of gene expression using phylogenetic comparative methods

BACKGROUND

Researchers often measure changes in gene expression across conditions to better understand the shared functional roles and regulatory mechanisms of different genes. Analogous to this is comparing gene expression across species, which can improve our understanding of the evolutionary processes shaping the evolution of both individual genes and functional pathways. One area of interest is determining genes showing signals of coevolution, which can also indicate potential functional similarity, analogous to co-expression analysis often performed across conditions for a single species. However, as with any trait, comparing gene expression across species can be confounded by the non-independence of species due to shared ancestry, making standard hypothesis testing inappropriate.

RESULTS

We compared RNA-Seq data across 18 fungal species using a multivariate Brownian Motion phylogenetic comparative method (PCM), which allowed us to quantify coevolution between protein pairs while directly accounting for the shared ancestry of the species. Our work indicates proteins which physically-interact show stronger signals of coevolution than randomly-generated pairs. Interactions with stronger empirical and computational evidence also showing stronger signals of coevolution. We examined the effects of number of protein interactions and gene expression levels on coevolution, finding both factors are overall poor predictors of the strength of coevolution between a protein pair. Simulations further demonstrate the potential issues of analyzing gene expression coevolution without accounting for shared ancestry in a standard hypothesis testing framework. Furthermore, our simulations indicate the use of a randomly-generated null distribution as a means of determining statistical significance for detecting coevolving genes with phylogenetically-uncorrected correlations, as has previously been done, is less accurate than PCMs, although is a significant improvement over standard hypothesis testing. These methods are further improved by using a phylogenetically-corrected correlation metric.

CONCLUSIONS

Our work highlights potential benefits of using PCMs to detect gene expression coevolution from high-throughput omics scale data. This framework can be built upon to investigate other evolutionary hypotheses, such as changes in transcription regulatory mechanisms across species.

Novel phylogenetic methods are needed for understanding gene function in the era of mega-scale genome sequencing

Ongoing large-scale genome sequencing projects are forecasting a data deluge that will almost certainly overwhelm current analytical capabilities of evolutionary genomics. In contrast to population genomics, there are no standardized methods in evolutionary genomics for extracting evolutionary and functional (e.g. gene-trait association) signal from genomic data. Here, we examine how current practices of multi-species comparative genomics perform in this aspect and point out that many genomic datasets are under-utilized due to the lack of powerful methodologies. As a result, many current analyses emphasize gene families for which some functional data is already available, resulting in a growing gap between functionally well-characterized genes/organisms and the universe of unknowns. This leaves unknown genes on the 'dark side' of genomes, a problem that will not be mitigated by sequencing more and more genomes, unless we develop tools to infer functional hypotheses for unknown genes in a systematic manner. We provide an inventory of recently developed methods capable of predicting gene-gene and gene-trait associations based on comparative data, then argue that realizing the full potential of whole genome datasets requires the integration of phylogenetic comparative methods into genomics, a rich but underutilized toolbox for looking into the past.

Cross-Species Analysis of Single-Cell Transcriptomic Data

The ability to profile hundreds of thousands to millions of single cells using scRNA-sequencing has revolutionized the fields of cell and developmental biology, providing incredible insights into the diversity of forms and functions of cell types across many species. These technologies hold the promise of developing detailed cell type phylogenies which can describe the evolutionary and developmental relationships between cell types across species. This will require sampling of many species and taxa using single-cell transcriptomics, and methods to classify cell type homologies and diversifications. Many tools currently exist for analyzing single cell data and identifying cell types. However, cross-species comparisons are complicated by many biological and technical factors. These factors include batch effects common to deep-sequencing approaches, well known evolutionary relationships between orthologous and paralogous genes, and less well-understood evolutionary forces shaping transcriptome variation between species. In this review, I discuss recent developments in computational methods for the comparison of single-cell-omic data across species. These approaches have the potential to provide invaluable insight into how evolutionary forces act at the level of the cell and will further our understanding of the evolutionary origins of animal and cellular diversity.

The Evolution of Gene Expression Underlying Vision Loss in Cave Animals

Dissecting the evolutionary genetic processes underlying eye reduction and vision loss in obligate cave-dwelling organisms has been a long-standing challenge in evolutionary biology. Independent vision loss events in related subterranean organisms can provide critical insight into these processes as well as into the nature of convergent loss of complex traits. Advances in evolutionary developmental biology have illuminated the significant role of heritable gene expression variation in the evolution of new forms. Here, we analyze gene expression variation in adult eye tissue across the freshwater crayfish, representing four independent vision-loss events in caves. Species and individual expression patterns cluster by eye function rather than phylogeny, suggesting convergence in transcriptome evolution in independently blind animals. However, this clustering is not greater than what is observed in surface species with conserved eye function after accounting for phylogenetic expectations. Modeling expression evolution suggests that there is a common increase in evolutionary rates in the blind lineages, consistent with a relaxation of selective constraint maintaining optimal expression levels. This is evidence for a repeated loss of expression constraint in the transcriptomes of blind animals and that convergence occurs via a similar trajectory through genetic drift.

myTAI: evolutionary transcriptomics with R

Motivation

Next Generation Sequencing (NGS) technologies generate a large amount of high quality transcriptome datasets enabling the investigation of molecular processes on a genomic and metagenomic scale. These transcriptomics studies aim to quantify and compare the molecular phenotypes of the biological processes at hand. Despite the vast increase of available transcriptome datasets, little is known about the evolutionary conservation of those characterized transcriptomes.

Results

The myTAI package implements exploratory analysis functions to infer transcriptome conservation patterns in any transcriptome dataset. Comprehensive documentation of myTAI functions and tutorial vignettes provide step-by-step instructions on how to use the package in an exploratory and computationally reproducible manner.

Availability and implementation

The open source myTAI package is available at https://github.com/HajkD/myTAI and https://cran.r-project.org/web/packages/myTAI/index.html.

Supplementary information

Supplementary data are available at Bioinformatics online.

Comparative genomics and transcriptomics of host-pathogen interactions in insects: evolutionary insights and future directions

Classical evolutionary studies of protein-coding genes have established that genes in the canonical immune system are often among the most rapidly evolving within and between species. As more genomes and transcriptomes across insects are sequenced, it is becoming clear that duplications and losses of immune genes are also a likely consequence of host-pathogen interactions. Furthermore, particular species respond to diverse pathogenic challenges with a wide range of challenge-specific responses that are still poorly understood. Transcriptional studies, using RNA-seq to characterize the infection-regulated transcriptome of diverse insects, are crucial for additional progress in understanding the ecology and evolution of the full complexity of the host response.

A Penalized Likelihood Framework for High-Dimensional Phylogenetic Comparative Methods and an Application to New-World Monkeys Brain Evolution

Working with high-dimensional phylogenetic comparative data sets is challenging because likelihood-based multivariate methods suffer from low statistical performances as the number of traits $p $ approaches the number of species $n $ and because some computational complications occur when $p $ exceeds $n$. Alternative phylogenetic comparative methods have recently been proposed to deal with the large $p $ small $n $ scenario but their use and performances are limited. Herein, we develop a penalized likelihood (PL) framework to deal with high-dimensional comparative data sets. We propose various penalizations and methods for selecting the intensity of the penalties. We apply this general framework to the estimation of parameters (the evolutionary trait covariance matrix and parameters of the evolutionary model) and model comparison for the high-dimensional multivariate Brownian motion (BM), Early-burst (EB), Ornstein-Uhlenbeck (OU), and Pagel's lambda models. We show using simulations that our PL approach dramatically improves the estimation of evolutionary trait covariance matrices and model parameters when $p$ approaches $n$, and allows for their accurate estimation when $p$ equals or exceeds $n$. In addition, we show that PL models can be efficiently compared using generalized information criterion (GIC). We implement these methods, as well as the related estimation of ancestral states and the computation of phylogenetic principal component analysis in the R package RPANDA and mvMORPH. Finally, we illustrate the utility of the new proposed framework by evaluating evolutionary models fit, analyzing integration patterns, and reconstructing evolutionary trajectories for a high-dimensional 3D data set of brain shape in the New World monkeys. We find a clear support for an EB model suggesting an early diversification of brain morphology during the ecological radiation of the clade. PL offers an efficient way to deal with high-dimensional multivariate comparative data.

Reanalyzing the Palaeoptera problem - The origin of insect flight remains obscure

The phylogenetic relationships of the winged insect lineages - mayflies (Ephemeroptera), damselflies and dragonflies (Odonata), and all other winged insects (Neoptera) - are still controversial with three hypotheses supported by different datasets: Palaeoptera, Metapterygota and Chiastomyaria. Here, we reanalyze available phylogenomic data with a focus on detecting confounding and alternative signal. In this context, we provide a framework to quantitatively evaluate and assess incongruent molecular phylogenetic signal inherent in phylogenomic datasets. Despite overall support for the Palaeoptera hypothesis, we also found considerable signal for Chiastomyaria, which is not easily detectable by standardized tree inference approaches. Analyses of the accumulation of signal across gene partitions showed that signal accumulates gradually. However, even in case signal only slightly supported one over the other hypothesis, topologies inferred from large datasets switch from statistically strongly supported Palaeoptera to strongly supported Chiastomyaria. From a morphological point of view, Palaeoptera currently appears to be the best-supported hypothesis; however, recent analyses were restricted to head characters. Phylogenetic approaches covering all organ systems including analyses of potential functional or developmental convergence are still pending so that the Palaeoptera problem has to be considered an open question in insect systematics.

Paleogenomics of echinoids reveals an ancient origin for the double-negative specification of micromeres in sea urchins

Establishing a timeline for the evolution of novelties is a common, unifying goal at the intersection of evolutionary and developmental biology. Analyses of gene regulatory networks (GRNs) provide the ability to understand the underlying genetic and developmental mechanisms responsible for the origin of morphological structures both in the development of an individual and across entire evolutionary lineages. Accurately dating GRN novelties, thereby establishing a timeline for GRN evolution, is necessary to answer questions about the rate at which GRNs and their subcircuits evolve, and to tie their evolution to paleoenvironmental and paleoecological changes. Paleogenomics unites the fossil record and all aspects of deep time, with modern genomics and developmental biology to understand the evolution of genomes in evolutionary time. Recent work on the regulatory genomic basis of development in cidaroid echinoids, sand dollars, heart urchins, and other nonmodel echinoderms provides an ideal dataset with which to explore GRN evolution in a comparative framework. Using divergence time estimation and ancestral state reconstructions, we have determined the age of the double-negative gate (DNG), the subcircuit which specifies micromeres and skeletogenic cells in Strongylocentrotus purpuratus We have determined that the DNG has likely been used for euechinoid echinoid micromere specification since at least the Late Triassic. The innovation of the DNG thus predates the burst of post-Paleozoic echinoid morphological diversification that began in the Early Jurassic. Paleogenomics has wide applicability for the integration of deep time and molecular developmental data, and has wide utility in rigorously establishing timelines for GRN evolution.

Animal Evolution: Are Phyla Real?

A recent study compares developmental gene expression among very distantly related animal species and interprets the results as providing biological justification for phyla. Several problems in the design and interpretation of the study call this conclusion into question.

Shifts in gene expression profiles are associated with weak and strong Crassulacean acid metabolism

PREMISE OF THE STUDY

The relative ease of high throughput sequencing is facilitating comprehensive phylogenomic and gene expression studies, even for nonmodel groups. To date, however, these two approaches have not been merged; while phylogenomic methods might use transcriptome sequences to resolve relationships, assessment of gene expression patterns in a phylogenetic context is less common. Here we analyzed both carbon assimilation and gene expression patterns of closely related species within the Agavoideae (Asparagaceae) to elucidate changes in gene expression across weak and strong phenotypes for Crassulacean acid metabolism (CAM).

METHODS

Gene expression patterns were compared across four genera: Agave (CAM), which is paraphyletic with Polianthes (weak CAM) and Manfreda (CAM), and Beschorneria (weak CAM). RNA-sequencing was paired with measures of gas exchange and titratable acidity. Climate niche space was compared across the four lineages to examine abiotic factors and their correlation to CAM.

KEY RESULTS

Expression of homologous genes showed both shared and variable patterns in weak and strong CAM species. Network analysis highlights that despite shared expression patterns, highly connected genes differ between weak and strong CAM, implicating shifts in regulatory gene function as key for the evolution of CAM. Variation in carbohydrate metabolism between weak and strong CAM supports the importance of sugar turnovers for CAM physiology.

CONCLUSIONS

Integration of phylogenetics and RNA-sequencing provides a powerful tool to study the evolution of CAM photosynthesis across closely related but photosynthetically variable species. Our findings regarding shared or shifted gene expression and regulation of CAM via carbohydrate metabolism have important implications for efforts to engineer the CAM pathway into C₃ food and biofuel crops.

Pairwise comparisons across species are problematic when analyzing functional genomic data

There is considerable interest in comparing functional genomic data across species. One goal of such work is to provide an integrated understanding of genome and phenotype evolution. Most comparative functional genomic studies have relied on multiple pairwise comparisons between species, an approach that does not incorporate information about the evolutionary relationships among species. The statistical problems that arise from not considering these relationships can lead pairwise approaches to the wrong conclusions and are a missed opportunity to learn about biology that can only be understood in an explicit phylogenetic context. Here, we examine two recently published studies that compare gene expression across species with pairwise methods, and find reason to question the original conclusions of both. One study interpreted pairwise comparisons of gene expression as support for the ortholog conjecture, the hypothesis that orthologs tend to have more similar attributes (expression in this case) than paralogs. The other study interpreted pairwise comparisons of embryonic gene expression across distantly related animals as evidence for a distinct evolutionary process that gave rise to phyla. In each study, distinct patterns of pairwise similarity among species were originally interpreted as evidence of particular evolutionary processes, but instead, we find that they reflect species relationships. These reanalyses concretely show the inadequacy of pairwise comparisons for analyzing functional genomic data across species. It will be critical to adopt phylogenetic comparative methods in future functional genomic work. Fortunately, phylogenetic comparative biology is also a rapidly advancing field with many methods that can be directly applied to functional genomic data.

Complexity and conservation of regulatory landscapes underlie evolutionary resilience of mammalian gene expression

To gain insight into how mammalian gene expression is controlled by rapidly evolving regulatory elements, we jointly analysed promoter and enhancer activity with downstream transcription levels in liver samples from 15 species. Genes associated with complex regulatory landscapes generally exhibit high expression levels that remain evolutionarily stable. While the number of regulatory elements is the key driver of transcriptional output and resilience, regulatory conservation matters: elements active across mammals most effectively stabilize gene expression. In contrast, recently evolved enhancers typically contribute weakly, consistent with their high evolutionary plasticity. These effects are observed across the entire mammalian clade and are robust to potential confounders, such as the gene expression level. Using liver as a representative somatic tissue, our results illuminate how the evolutionary stability of gene expression is profoundly entwined with both the number and conservation of surrounding promoters and enhancers.

Indoles induce metamorphosis in a broad diversity of jellyfish, but not in a crown jelly (Coronatae)

Many animals go through one or more metamorphoses during their lives, however, the molecular underpinnings of metamorphosis across diverse species are not well understood. Medusozoa (Cnidaria) is a clade of animals with complex life cycles, these life cycles can include a polyp stage that metamorphoses into a medusa (jellyfish). Medusae are produced through a variety of different developmental mechanisms-in some species polyps bud medusae (Hydrozoa), in others medusae are formed through polyp fission (Scyphozoa), while in others medusae are formed through direct transformation of the polyp (Cubozoa). To better understand the molecular mechanisms that may coordinate these different forms of metamorphosis, we tested two compounds first identified to induce metamorphosis in the moon jellyfish Aurelia aurita (indomethacin and 5-methoxy-2-methylindole) on a broad diversity of medusozoan polyps. We discovered that indole-containing compounds trigger metamorphosis across a broad diversity of species. All tested discomedusan polyps metamorphosed in the presence of both compounds, including species representatives of several major lineages within the clade (Pelagiidae, Cyaneidae, both clades of Rhizostomeae). In a cubozoan, low levels of 5-methoxy-2-methylindole reliably induced complete and healthy metamorphosis. In contrast, neither compound induced medusa metamorphosis in a coronate scyphozoan, or medusa production in either hydrozoan tested. Our results support the hypothesis that metamorphosis is mediated by a conserved induction pathway within discomedusan scyphozoans, and possibly cubozoans. However, failure of these compounds to induce metamorphosis in a coronate suggests this induction mechanism may have been lost in this clade, or is convergent between Scyphozoa and Cubozoa.

Genome-Guided Phylo-Transcriptomic Methods and the Nuclear Phylogentic Tree of the Paniceae Grasses

The past few years have witnessed a paradigm shift in molecular systematics from phylogenetic methods (using one or a few genes) to those that can be described as phylogenomics (phylogenetic inference with entire genomes). One approach that has recently emerged is phylo-transcriptomics (transcriptome-based phylogenetic inference). As in any phylogenetics experiment, accurate orthology inference is critical to phylo-transcriptomics. To date, most analyses have inferred orthology based either on pure sequence similarity or using gene-tree approaches. The use of conserved genome synteny in orthology detection has been relatively under-employed in phylogenetics, mainly due to the cost of sequencing genomes. While current trends focus on the quantity of genes included in an analysis, the use of synteny is likely to improve the quality of ortholog inference. In this study, we combine de novo transcriptome data and sequenced genomes from an economically important group of grass species, the tribe Paniceae, to make phylogenomic inferences. This method, which we call “genome-guided phylo-transcriptomics”, is compared to other recently published orthology inference pipelines, and benchmarked using a set of sequenced genomes from across the grasses. These comparisons provide a framework for future researchers to evaluate the costs and benefits of adding sequenced genomes to transcriptome data sets.

Molecular signatures of longevity: Insights from cross-species comparative studies

Much of the current research on longevity focuses on the aging process within a single species. Several molecular players (e.g. IGF1 and MTOR), pharmacological compounds (e.g. rapamycin and metformin), and dietary approaches (e.g. calorie restriction and methionine restriction) have been shown to be important in regulating and modestly extending lifespan in model organisms. On the other hand, natural lifespan varies much more significantly across species. Within mammals alone, maximum lifespan differs more than 100 fold, but the underlying regulatory mechanisms remain poorly understood. Recent comparative studies are beginning to shed light on the molecular signatures associated with exceptional longevity. These include genome sequencing of microbats, naked mole rat, blind mole rat, bowhead whale and African turquoise killifish, and comparative analyses of gene expression, metabolites, lipids and ions across multiple mammalian species. Together, they point towards several putative strategies for lifespan regulation and cancer resistance, as well as the pathways and metabolites associated with longevity variation. In particular, longevity may be achieved by both lineage-specific adaptations and common mechanisms that apply across the species. Comparing the resulting cross-species molecular signatures with the within-species lifespan extension strategies will improve our understanding of mechanisms of longevity control and provide a starting point for novel and effective interventions.

Studying the evolutionary significance of thermal adaptation in ectotherms: The diversification of amphibians' energetics

A fundamental problem in evolutionary biology is the understanding of the factors that promote or constrain adaptive evolution, and assessing the role of natural selection in this process. Here, comparative phylogenetics, that is, using phylogenetic information and traits to infer evolutionary processes has been a major paradigm . In this study, we discuss Ornstein-Uhlenbeck models (OU) in the context of thermal adaptation in ectotherms. We specifically applied this approach to study amphibians's evolution and energy metabolism. It has been hypothesized that amphibians exploit adaptive zones characterized by low energy expenditure, which generate specific predictions in terms of the patterns of diversification in standard metabolic rate (SMR). We complied whole-animal metabolic rates for 122 species of amphibians, and adjusted several models of diversification. According to the adaptive zone hypothesis, we expected: (1) to find "accelerated evolution" in SMR (i.e., diversification above Brownian Motion expectations, BM), (2) that a model assuming evolutionary optima (i.e., an OU model) fits better than a white-noise model and (3) that a model assuming multiple optima (according to the three amphibians's orders) fits better than a model assuming a single optimum. As predicted, we found that the diversification of SMR occurred most of the time, above BM expectations. Also, we found that a model assuming an optimum explained the data in a better way than a white-noise model. However, we did not find evidence that an OU model with multiple optima fits the data better, suggesting a single optimum in SMR for Anura, Caudata and Gymnophiona. These results show how comparative phylogenetics could be applied for testing adaptive hypotheses regarding history and physiological performance in ectotherms.

Mitochondrial selfish elements and the evolution of biological novelties

We report the present knowledge about RPHM21, a novel male-specific mitochondrial protein with a putative role in the paternal inheritance of sperm mitochondria in the Manila clam Ruditapes philippinarum, a species with doubly uniparental inheritance of mitochondria (DUI). We review all the available data on rphm21 transcription and translation, analyze in detail its female counterpart, RPHF22, discuss the homology with RPHM21, the putative function and origin, and analyze their polymorphism. The available evidence is compatible with a viral origin of RPHM21 and supports its activity during spermatogenesis. RPHM21 is progressively accumulated in mitochondria and nuclei of spermatogenic cells, and we hypothesize it can influence mitochondrial inheritance and sexual differentiation. We propose a testable model that describes how the acquisition of selfish features by a mitochondrial lineage might have been responsible for the emergence of DUI, and for the evolution of separate sexes (gonochorism) from hermaphroditism. The appearance of DUI most likely entailed the invasion of at least 1 selfish element, and the extant DUI systems can be seen as resolved conflicts. It was proposed that hermaphroditism was the ancestral condition of bivalves, and a correlation between DUI and gonochorism was documented. We hypothesize that DUI might have driven the shift from hermaphroditism to gonochorism, with androdioecy as transition state. The invasion of sex-ratio distorters and the evolution of suppressors can prompt rapid changes among sex-determination mechanisms, and DUI might have been responsible for one of such changes in some bivalve species. If true, DUI would represent the first animal sex-determination system involving mtDNA-encoded proteins.

The evolution of animal genomes

Genome sequences are now available for hundreds of species sampled across the animal phylogeny, bringing key features of animal genome evolution into sharper focus. The field of animal evolutionary genomics has focused on identifying and classifying the diversity genomic features, reconstructing the history of evolutionary changes in animal genomes, and testing hypotheses about the evolutionary relationships of animals. The grand challenges moving forward are to connect evolutionary changes in genomes with particular evolutionary changes in phenotypes, and to determine which changes are driven by selection. This will require far greater genome sampling both across and within species, extensive phenotype data, a well resolved animal phylogeny, and advances in comparative methods.

Interspecific Differential Expression Analysis of RNA-Seq Data Yields Insight into Life Cycle Variation in Hydractiniid Hydrozoans

Hydrozoans are known for their complex life cycles, which can alternate between an asexually reproducing polyp stage and a sexually reproducing medusa stage. Most hydrozoan species, however, lack a free-living medusa stage and instead display a developmentally truncated form, called a medusoid or sporosac, which generally remains attached to the polyp. Although evolutionary transitions in medusa truncation and loss have been investigated phylogenetically, little is known about the genes involved in the development and loss of this life cycle stage. Here, we present a new workflow for evaluating differential expression (DE) between two species using short read Illumina RNA-seq data. Through interspecific DE analyses between two hydractiniid hydrozoans, Hydractinia symbiolongicarpus and Podocoryna carnea, we identified genes potentially involved in the developmental, functional, and morphological differences between the fully developed medusa of P. carnea and reduced sporosac of H. symbiolongicarpus. A total of 10,909 putative orthologs of H. symbiolongicarpus and P. carnea were identified from de novo assemblies of short read Illumina data. DE analysis revealed 938 of these are differentially expressed between P. carnea developing and adult medusa, when compared with H. symbiolongicarpus sporosacs, the majority of which have not been previously characterized in cnidarians. In addition, several genes with no corresponding ortholog in H. symbiolongicarpus were expressed in developing medusa of P. carnea. Results presented here show interspecific DE analyses of RNA-seq data to be a sensitive and reliable method for identifying genes and gene pathways potentially involved in morphological and life cycle differences between species.

Phylogenetic ANOVA: The Expression Variance and Evolution Model for Quantitative Trait Evolution

A number of methods have been developed for modeling the evolution of a quantitative trait on a phylogeny. These methods have received renewed interest in the context of genome-wide studies of gene expression, in which the expression levels of many genes can be modeled as quantitative traits. We here develop a new method for joint analyses of quantitative traits within- and between species, the Expression Variance and Evolution (EVE) model. The model parameterizes the ratio of population to evolutionary expression variance, facilitating a wide variety of analyses, including a test for lineage-specific shifts in expression level, and a phylogenetic ANOVA that can detect genes with increased or decreased ratios of expression divergence to diversity, analogous to the famous Hudson Kreitman Aguadé (HKA) test used to detect selection at the DNA level. We use simulations to explore the properties of these tests under a variety of circumstances and show that the phylogenetic ANOVA is more accurate than the standard ANOVA (no accounting for phylogeny) sometimes used in transcriptomics. We then apply the EVE model to a mammalian phylogeny of 15 species typed for expression levels in liver tissue. We identify genes with high expression divergence between species as candidates for expression level adaptation, and genes with high expression diversity within species as candidates for expression level conservation and/or plasticity. Using the test for lineage-specific expression shifts, we identify several candidate genes for expression level adaptation on the catarrhine and human lineages, including genes putatively related to dietary changes in humans. We compare these results to those reported previously using a model which ignores expression variance within species, uncovering important differences in performance. We demonstrate the necessity for a phylogenetic model in comparative expression studies and show the utility of the EVE model to detect expression divergence, diversity, and branch-specific shifts.