Amalgamated cross-species transcriptomes reveal organ-specific propensity in gene expression evolution

Network properties constrain natural selection on gene expression in Caenorhabditis elegans

Gene regulatory networks (GRNs) integrate genetic and environmental signals to coordinate complex phenotypes and evolve through a balance of selection and drift. Using publicly available datasets from Caenorhabditis elegans, we investigated the extent of natural selection on transcript abundance by linking population-scale variation in gene expression to fecundity, a key fitness component. While the expression of most genes covaried only weakly with fitness, which is typical for polygenic traits, we identified seven transcripts under significant directional selection. These included nhr-114 and feh-1, implicating variation in nutrient-sensing and metabolic pathways as impacting fitness. Stronger directional selection on tissue-specific and older genes highlighted the germline and nervous system as focal points of adaptive change. Network position further constrained selection on gene expression; high-connectivity genes faced stronger stabilizing and directional selection, highlighting GRN architecture as a key factor in microevolutionary dynamics. The activity of transcription factors such as zip-3, which regulates mitochondrial stress responses, emerged as targets of selection, revealing potential links between energy homeostasis and fitness. Our findings demonstrate how GRNs mediate the interplay between selection and drift, shaping microevolutionary trajectories of gene expression and phenotypic diversity.

Expression-based machine learning models for predicting plant tissue identity

Premise

The selection of Arabidopsis as a model organism played a pivotal role in advancing genomic science. The competing frameworks to select an agricultural- or ecological-based model species were rejected, in favor of building knowledge in a species that would facilitate genome-enabled research.

Methods

Here, we examine the ability of models based on Arabidopsis gene expression data to predict tissue identity in other flowering plants. Comparing different machine learning algorithms, models trained and tested on Arabidopsis data achieved near perfect precision and recall values, whereas when tissue identity is predicted across the flowering plants using models trained on Arabidopsis data, precision values range from 0.69 to 0.74 and recall from 0.54 to 0.64.

Results

The identity of belowground tissue can be predicted more accurately than other tissue types, and the ability to predict tissue identity is not correlated with phylogenetic distance from Arabidopsis. k-nearest neighbors is the most successful algorithm, suggesting that gene expression signatures, rather than marker genes, are more valuable to create models for tissue and cell type prediction in plants.

Discussion

Our data-driven results highlight that the assertion that knowledge from Arabidopsis is translatable to other plants is not always true. Considering the current landscape of abundant sequencing data, we should reevaluate the scientific emphasis on Arabidopsis and prioritize plant diversity.

Functional and regulatory diversification of Period genes responsible for circadian rhythm in vertebrates

The Period genes (Per) play essential roles in modulating the molecular circadian clock timing in a broad range of species, which regulates the physiological and cellular rhythms through the transcription-translation feedback loop. While the Period gene paralogs are widely observed among vertebrates, the evolutionary history and the functional diversification of Per genes across vertebrates are not well known. In this study, we comprehensively investigated the evolution of Per genes at the copy number and sequence levels, including de novo binding motif discovery by comparative genomics. We also determined the lineage-specific transcriptome landscape across tissues and developmental stages and phenotypic effects in public RNA-seq data sets of model species. We observed multiple lineage-specific gain and loss events Per genes, though no simple association was observed between ecological factors and Per gene numbers in each species. Among salmonid fish species, the per3 gene has been lost in the majority, whereas those retaining the per3 gene exhibit not a signature of relaxed selective constraint but rather a signature of intensified selection. We also determined the signature of adaptive diversification of the CRY-binding region in Per1 and Per3, which modulates the circadian rhythm. We also discovered putative regulatory sequences, which are lineage-specific, suggesting that these cis-regulatory elements may have evolved rapidly and divergently across different lineages. Collectively, our findings revealed the evolution of Per genes and their fine-tuned contribution to the plastic and precise regulation of circadian rhythms in various vertebrate taxa.

From whole bodies to single cells: A guide to transcriptomic approaches for ecology and evolutionary biology

RNA sequencing (RNAseq) methodology has experienced a burst of technological developments in the last decade, which has opened up opportunities for studying the mechanisms of adaptation to environmental factors at both the organismal and cellular level. Selecting the most suitable experimental approach for specific research questions and model systems can, however, be a challenge and researchers in ecology and evolution are commonly faced with the choice of whether to study gene expression variation in whole bodies, specific tissues, and/or single cells. A wide range of sometimes polarised opinions exists over which approach is best. Here, we highlight the advantages and disadvantages of each of these approaches to provide a guide to help researchers make informed decisions and maximise the power of their study. Using illustrative examples of various ecological and evolutionary research questions, we guide the readers through the different RNAseq approaches and help them identify the most suitable design for their own projects.

Evolution of tissue-specific expression of ancestral genes across vertebrates and insects

Regulation of gene expression is arguably the main mechanism underlying the phenotypic diversity of tissues within and between species. Here we assembled an extensive transcriptomic dataset covering 8 tissues across 20 bilaterian species and performed analyses using a symmetric phylogeny that allowed the combined and parallel investigation of gene expression evolution between vertebrates and insects. We specifically focused on widely conserved ancestral genes, identifying strong cores of pan-bilaterian tissue-specific genes and even larger groups that diverged to define vertebrate and insect tissues. Systematic inferences of tissue-specificity gains and losses show that nearly half of all ancestral genes have been recruited into tissue-specific transcriptomes. This occurred during both ancient and, especially, recent bilaterian evolution, with several gains being associated with the emergence of unique phenotypes (for example, novel cell types). Such pervasive evolution of tissue specificity was linked to gene duplication coupled with expression specialization of one of the copies, revealing an unappreciated prolonged effect of whole-genome duplications on recent vertebrate evolution.

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.

Role of paralogs in the sex-bias transcriptional and metabolic regulation of the brain-placental axis in mice

INTRODUCTION

Duplicated genes or paralogs play important roles in the adaptive function of eukaryotic genomes. Animal studies have shown evidence for the functional role of paralogs in pregnancy, but our knowledge about the role of paralogs in the fetoplacental regulation remains limited. In particular, if fetoplacental metabolic regulation is modulated by differential expression of paralogs remains unexamined.

METHODS

In this study, gene expression profiles of day-15 placenta and fetal brain were compared to identify families or groups of paralogous genes expressed in the placenta and brain of male versus female fetuses in mice. A Bayesian modeling was applied to infer directional relationship of transcriptional variation of the paralogs relative to the phylogenetic variation of the genes in each family. Gas chromatography-mass spectrometry (GC-MS) was used to perform untargeted metabolomics analysis of day-15 placenta and fetal brain of both sexes.

RESULTS

We identified paralog groups that were expressed in a sex and/or tissue biased manner between the placenta and fetal brain. Bayesian modeling showed evidence for directional relationship between expression and phylogeny of specific paralogs. These relationships were sex specific. GC-MS analysis identified metabolites that were expressed in a sex-bias manner between the placenta and fetal brain. By performing integrative analysis of the metabolomics and gene expression data, we showed that specific groups of metabolites and paralogous genes were expressed in a coordinated manner between the placenta and fetal brain.

DISCUSSION

The findings of this study collectively suggest that paralogs play an influential role in the regulation of the brain-placental axis in mice.

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.

Subgenome dominance shapes novel gene evolution in the decaploid pitcher plant Nepenthes gracilis

Subgenome dominance after whole-genome duplication generates distinction in gene number and expression at the level of chromosome sets, but it remains unclear how this process may be involved in evolutionary novelty. Here we generated a chromosome-scale genome assembly of the Asian pitcher plant Nepenthes gracilis to analyse how its novel traits (dioecy and carnivorous pitcher leaves) are linked to genomic evolution. We found a decaploid karyotype and a clear indication of subgenome dominance. A male-linked and pericentromerically located region on the putative sex chromosome was identified in a recessive subgenome and was found to harbour three transcription factors involved in flower and pollen development, including a likely neofunctionalized LEAFY duplicate. Transcriptomic and syntenic analyses of carnivory-related genes suggested that the paleopolyploidization events seeded genes that subsequently formed tandem clusters in recessive subgenomes with specific expression in the digestive zone of the pitcher, where specialized cells digest prey and absorb derived nutrients. A genome-scale analysis suggested that subgenome dominance likely contributed to evolutionary innovation by permitting recessive subgenomes to diversify functions of novel tissue-specific duplicates. Our results provide insight into how polyploidy can give rise to novel traits in divergent and successful high-ploidy lineages.

Exploring the genetic landscape of nitrogen uptake in durum wheat: genome-wide characterization and expression profiling of NPF and NRT2 gene families

Nitrate uptake by plants primarily relies on two gene families: Nitrate transporter 1/peptide transporter (NPF) and Nitrate transporter 2 (NRT2). Here, we extensively characterized the NPF and NRT2 families in the durum wheat genome, revealing 211 NPF and 20 NRT2 genes. The two families share many Cis Regulatory Elements (CREs) and Transcription Factor binding sites, highlighting a partially overlapping regulatory system and suggesting a coordinated response for nitrate transport and utilization. Analyzing RNA-seq data from 9 tissues and 20 cultivars, we explored expression profiles and co-expression relationships of both gene families. We observed a strong correlation between nucleotide variation and gene expression within the NRT2 gene family, implicating a shared selection mechanism operating on both coding and regulatory regions. Furthermore, NPF genes showed highly tissue-specific expression profiles, while NRT2s were mainly divided in two co-expression modules, one expressed in roots (NAR2/NRT3 dependent) and the other induced in anthers and/ovaries during maturation. Our evidences confirmed that the majority of these genes were retained after small-scale duplication events, suggesting a neo- or sub-functionalization of many NPFs and NRT2s. Altogether, these findings indicate that the expansion of these gene families in durum wheat could provide valuable genetic variability useful to identify NUE-related and candidate genes for future breeding programs in the context of low-impact and sustainable agriculture.

CD44 correlates with longevity and enhances basal ATF6 activity and ER stress resistance

The naked mole rat (NMR) is the longest-lived rodent, resistant to multiple age-related diseases including neurodegeneration. However, the mechanisms underlying the NMR's resistance to neurodegenerative diseases remain elusive. Here, we isolated oligodendrocyte progenitor cells (OPCs) from NMRs and compared their transcriptome with that of other mammals. Extracellular matrix (ECM) genes best distinguish OPCs of long- and short-lived species. Notably, expression levels of CD44, an ECM-binding protein that has been suggested to contribute to NMR longevity by mediating the effect of hyaluronan (HA), are not only high in OPCs of long-lived species but also positively correlate with longevity in multiple cell types/tissues. We found that CD44 localizes to the endoplasmic reticulum (ER) and enhances basal ATF6 activity. CD44 modifies proteome and membrane properties of the ER and enhances ER stress resistance in a manner dependent on unfolded protein response regulators without the requirement of HA. HA-independent role of CD44 in proteostasis regulation may contribute to mammalian longevity.

Large-scale across species transcriptomic analysis identifies genetic selection signatures associated with longevity in mammals

Lifespan varies significantly among mammals, with more than 100-fold difference between the shortest and longest living species. This natural difference may uncover the evolutionary forces and molecular features that define longevity. To understand the relationship between gene expression variation and longevity, we conducted a comparative transcriptomics analysis of liver, kidney, and brain tissues of 103 mammalian species. We found that few genes exhibit common expression patterns with longevity in the three organs analyzed. However, pathways related to translation fidelity, such as nonsense-mediated decay and eukaryotic translation elongation, correlated with longevity across mammals. Analyses of selection pressure found that selection intensity related to the direction of longevity-correlated genes is inconsistent across organs. Furthermore, expression of methionine restriction-related genes correlated with longevity and was under strong selection in long-lived mammals, suggesting that a common strategy is utilized by natural selection and artificial intervention to control lifespan. Our results indicate that lifespan regulation via gene expression is driven through polygenic and indirect natural selection.

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.

Gene expressions associated with longer lifespan and aging exhibit similarity in mammals

Although molecular features underlying aging and species maximum lifespan (MLS) have been comprehensively studied by transcriptome analyses, the actual impact of transcriptome on aging and MLS remains elusive. Here, we found that transcriptional signatures that are associated with mammalian MLS exhibited significant similarity to those of aging. Moreover, transcriptional signatures of longer MLS and aging both exhibited significant similarity to that of longer-lived mouse strains, suggesting that gene expression patterns associated with species MLS contribute to extended lifespan even within a species and that aging-related gene expression changes overall represent adaptations that extend lifespan rather than deterioration. Finally, we found evidence of co-evolution of MLS and promoter sequences of MLS-associated genes, highlighting the evolutionary contribution of specific transcription factor binding motifs such as that of E2F1 in shaping MLS-associated gene expression signature. Our results highlight the importance of focusing on adaptive aspects of aging transcriptome and demonstrate that cross-species genomics can be a powerful approach for understanding adaptive aging transcriptome.

Phylotranscriptomics supports numerous polyploidization events and phylogenetic relationships in Nicotiana

Introduction

Nicotiana L. (Solanaceae) is of great scientific and economic importance, and polyploidization has been pivotal in shaping this genus. Despite many previous studies on the Nicotiana phylogenetic relationship and hybridization, evidence from whole genome data is still lacking.

Methods

In this study, we obtained 995 low-copy genes and plastid transcript fragments from the transcriptome datasets of 26 Nicotiana species, including all sections. We reconstructed the phylogenetic relationship and phylogenetic network of diploid species.

Results

The incongruence among gene trees showed that the formation of N. sylvestris involved incomplete lineage sorting. The nuclear-plastid discordance and nuclear introgression absence indicated that organelle capture from section Trigonophyllae was involved in forming section Petunioides. Furthermore, we analyzed the evolutionary origin of polyploid species and dated the time of hybridization events based on the analysis of PhyloNet, sequence similarity search, and phylogeny of subgenome approaches. Our results highly evidenced the hybrid origins of five polyploid sections, including sections Nicotiana, Repandae, Rusticae, Polydicliae, and Suaveolentes. Notably, we provide novel insights into the hybridization event of section Polydicliae and Suaveolentes. The section Polydicliae formed from a single hybridization event between maternal progenitor N. attenuata and paternal progenitor N. undulata; the N. sylvestris (paternal progenitor) and the N. glauca (maternal progenitor) were involved in the formation of section Suaveolentes.

Discussion

This study represents the first exploration of Nicotiana polyploidization events and phylogenetic relationships using the high-throughput RNA-seq approach. It will provide guidance for further studies in molecular systematics, population genetics, and ecological adaption studies in Nicotiana and other related species.

Integrative mapping of the dog epigenome: Reference annotation for comparative intertissue and cross-species studies

Dogs have become a valuable model in exploring multifaceted diseases and biology relevant to human health. Despite large-scale dog genome projects producing high-quality draft references, a comprehensive annotation of functional elements is still lacking. We addressed this through integrative next-generation sequencing of transcriptomes paired with five histone marks and DNA methylome profiling across 11 tissue types, deciphering the dog's epigenetic code by defining distinct chromatin states, super-enhancer, and methylome landscapes, and thus showed that these regions are associated with a wide range of biological functions and cell/tissue identity. In addition, we confirmed that the phenotype-associated variants are enriched in tissue-specific regulatory regions and, therefore, the tissue of origin of the variants can be traced. Ultimately, we delineated conserved and dynamic epigenomic changes at the tissue- and species-specific resolutions. Our study provides an epigenomic blueprint of the dog that can be used for comparative biology and medical research.

Weaker selection on genes with treatment-specific expression consistent with a limit on plasticity evolution in Arabidopsis thaliana

Differential gene expression between environments often underlies phenotypic plasticity. However, environment-specific expression patterns are hypothesized to relax selection on genes, and thus limit plasticity evolution. We collated over 27 terabases of RNA-sequencing data on Arabidopsis thaliana from over 300 peer-reviewed studies and 200 treatment conditions to investigate this hypothesis. Consistent with relaxed selection, genes with more treatment-specific expression have higher levels of nucleotide diversity and divergence at nonsynonymous sites but lack stronger signals of positive selection. This result persisted even after controlling for expression level, gene length, GC content, the tissue specificity of expression, and technical variation between studies. Overall, our investigation supports the existence of a hypothesized trade-off between the environment specificity of a gene's expression and the strength of selection on said gene in A. thaliana. Future studies should leverage multiple genome-scale datasets to tease apart the contributions of many variables in limiting plasticity evolution.

Analyses of Genome Regulatory Evolution Following Whole-Genome Duplication Using the Phylogenetic EVE Model

Whole-genome duplications (WGDs) are important in shaping the evolution of complex genomes, including rewiring of genome regulation. To address key questions about how WGDs impact the evolution of genome regulation, we need to understand the relative importance of selection versus drift and temporal evolutionary dynamics. One promising class of statistical models that can help address such questions are phylogenetic Ornstein-Uhlenbeck (OU) models.Here we present a computational pipeline for the comparative phylogenetic analyses of genome regulation using an OU model. We have implemented this model in R and provide a step-by-step protocol for the use of this model, including example scripts and simulated test data. We provide the nonspecialist a brief overview of how this model works and how to perform tests for signatures of selection on genome regulation as well as power simulations to aid in experimental design and interpretation of results. We believe that these resources could help polyploidy research move forward in an era of rapidly increasing functional genomics data across the tree of life.

Machine learning enables prediction of metabolic system evolution in bacteria

Evolution prediction is a long-standing goal in evolutionary biology, with potential impacts on strategic pathogen control, genome engineering, and synthetic biology. While laboratory evolution studies have shown the predictability of short-term and sequence-level evolution, that of long-term and system-level evolution has not been systematically examined. Here, we show that the gene content evolution of metabolic systems is generally predictable by applying ancestral gene content reconstruction and machine learning techniques to ~3000 bacterial genomes. Our framework, Evodictor, successfully predicted gene gain and loss evolution at the branches of the reference phylogenetic tree, suggesting that evolutionary pressures and constraints on metabolic systems are universally shared. Investigation of pathway architectures and meta-analysis of metagenomic datasets confirmed that these evolutionary patterns have physiological and ecological bases as functional dependencies among metabolic reactions and bacterial habitat changes. Last, pan-genomic analysis of intraspecies gene content variations proved that even "ongoing" evolution in extant bacterial species is predictable in our framework.

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.

Detecting macroevolutionary genotype-phenotype associations using error-corrected rates of protein convergence

On macroevolutionary timescales, extensive mutations and phylogenetic uncertainty mask the signals of genotype-phenotype associations underlying convergent evolution. To overcome this problem, we extended the widely used framework of non-synonymous to synonymous substitution rate ratios and developed the novel metric ω_C, which measures the error-corrected convergence rate of protein evolution. While ω_C distinguishes natural selection from genetic noise and phylogenetic errors in simulation and real examples, its accuracy allows an exploratory genome-wide search of adaptive molecular convergence without phenotypic hypothesis or candidate genes. Using gene expression data, we explored over 20 million branch combinations in vertebrate genes and identified the joint convergence of expression patterns and protein sequences with amino acid substitutions in functionally important sites, providing hypotheses on undiscovered phenotypes. We further extended our method with a heuristic algorithm to detect highly repetitive convergence among computationally non-trivial higher-order phylogenetic combinations. Our approach allows bidirectional searches for genotype-phenotype associations, even in lineages that diverged for hundreds of millions of years.

Comparative transcriptome and metabolome analyses of four Panax species explore the dynamics of metabolite biosynthesis

Background

The genus Panax in the Araliaceae family has been used as traditional medicinal plants worldwide and is known to biosynthesize ginsenosides and phytosterols. However, genetic variation between Panax species has influenced their biosynthetic pathways is not fully understood.

Methods

Simultaneous analysis of transcriptomes and metabolomes obtained from adventitious roots of two tetraploid species (Panax ginseng and P. quinquefolius) and two diploid species (P. notoginseng and P. vietnamensis) revealed the diversity of their metabolites and related gene expression profiles.

Results

The transcriptome analysis showed that 2,3-OXIDOSQUALENE CYCLASEs (OSCs) involved in phytosterol biosynthesis are upregulated in the diploid species, while the expression of OSCs contributing to ginsenoside biosynthesis is higher in the tetraploid species. In agreement with these results, the contents of dammarenediol-type ginsenosides were higher in the tetraploid species relative to the diploid species.

Conclusion

These results suggest that a whole-genome duplication event has influenced the triterpene biosynthesis pathway in tetraploid Panax species during their evolution or ecological adaptation. This study provides a basis for further efforts to explore the genetic variation of the Panax genus.

Transcriptomic heterochrony and completely cleistogamous flower development in the mycoheterotrophic orchid Gastrodia

Cleistogamy, in which plants can reproduce via self-fertilization within permanently closed flowers, has evolved in > 30 angiosperm lineages; however, consistent with Darwin's doubts about its existence, complete cleistogamy - the production of only cleistogamous flowers - has rarely been recognized. Thus far, the achlorophyllous orchid genus, Gastrodia, is the only known genus with several plausible completely cleistogamous species. Here, we analyzed the floral developmental transcriptomes of two recently evolved, completely cleistogamous Gastrodia species and their chasmogamous sister species to elucidate the possible changes involved in producing common cleistogamous traits. The ABBA-BABA test did not support introgression and protein sequence convergence as evolutionary mechanisms leading to cleistogamy, leaving convergence in gene expression as a plausible mechanism. Regarding transcriptomic differentiation, the two cleistogamous species had common modifications in the expression of developmental regulators, exhibiting a gene family-wide signature of convergent expression changes in MADS-box genes. Our transcriptomic pseudotime analysis revealed a prolonged juvenile state and eventual maturation, a heterochronic pattern consistent with partial neoteny, in cleistogamous flower development. These findings indicate that transcriptomic partial neoteny, arising from changes in the expression of conserved developmental regulators, might have contributed to the rapid and repeated evolution of cleistogamous flowers in Gastrodia.

Divergence in interspecific and intersubspecific gene expression between two closely related horseshoe bats (Rhinolophus)

Closely related species have been used as representative systems to investigate the genetic mechanisms involved in the early stages of species differentiation. Previous studies have indicated that variation in gene expression might be a sensitive indicator of initial species divergence, although the role of expression divergence, and especially that associated with phenotypic variation remained relatively undefined. For three organs (cochlea, brain, and liver) from two closely related bat species (Rhinolophus siamensis and R. episcopus), the interspecific and intersubspecific gene expression profiles were compared using transcriptomics in this study. Striking organ specificity of expression was observed, and expression profiles exhibited similarities between cochlea and brain tissues. Numerous differentially expressed genes (DEGs) were identified for each organ in the interspecific comparison (cochlea/brain/liver: 1,069/647/692) and intersubspecific comparison (608/528/368). Functional enrichment analysis indicated vital variation in expression related to the immune system, ion activities, neuronal function, and multisensory system regulation in both comparisons. DEGs relevant to the variation in echolocation calls (RF) were found, and some of them were involved in the pivotal patterns of expression variation. The regulation of immune, ion channel, neural activity, and sophisticated sensory functions at the expression level might be key mechanisms in the early species divergence of bats, and the expression variation related to acoustical signal could have played a crucial part. This study expands our knowledge of gene expression and patterns of variation for three key organs to echolocation at both the interspecific and intersubspecific levels. Further, the framework described here provides insight into the genetic basis of phenotypic variation during the incipient stage of species differentiation.

Integrative cross-species analysis of GABAergic neuron cell types and their functions in Alzheimer's disease

Understanding the phenotypic and functional diversity of cerebral cortical GABAergic neurons requires a comprehensive analysis of key transcriptional signatures and neuronal subtype identity. However, the diversity and conservation of GABAergic neurons across multiple mammals remain unclear. Here, we collected the single-nucleus RNA sequencing (snRNA-seq) datasets of cerebral cortex from human, macaque, mouse, and pig to identify the conserved neuronal cell types across species. After systematic analysis of the heterogeneity of GABAergic neurons, we defined four major conserved GABAergic neuron subclasses (Inc SST, Inc LAMP5, Inc PVALB, and Inc VIP) across species. We characterized the species-enriched subclasses of GABAergic neurons from four mammals, such as Inc Meis2 in mouse. Then, we depicted the genetic regulatory network (GRNs) of GABAergic neuron subclasses, which showed the conserved and species-specific GRNs for GABAergic neuron cell types. Finally, we investigated the GABAergic neuron subclass-specific expression modules of Alzheimer's disease (AD)-related genes in GABAergic neuron cell types. Overall, our study reveals the conserved and divergent GABAergic neuron subclasses and GRNs across multiple species and unravels the gene expression modules of AD-risk genes in GABAergic neuron subclasses, facilitating the GABAergic neurons research and clinical treatment.

The landscape of transcriptional and translational changes over 22 years of bacterial adaptation

Organisms can adapt to an environment by taking multiple mutational paths. This redundancy at the genetic level, where many mutations have similar phenotypic and fitness effects, can make untangling the molecular mechanisms of complex adaptations difficult. Here, we use the Escherichia coli long-term evolution experiment (LTEE) as a model to address this challenge. To understand how different genomic changes could lead to parallel fitness gains, we characterize the landscape of transcriptional and translational changes across 12 replicate populations evolving in parallel for 50,000 generations. By quantifying absolute changes in mRNA abundances, we show that not only do all evolved lines have more mRNAs but that this increase in mRNA abundance scales with cell size. We also find that despite few shared mutations at the genetic level, clones from replicate populations in the LTEE are remarkably similar in their gene expression patterns at both the transcriptional and translational levels. Furthermore, we show that the majority of the expression changes are due to changes at the transcriptional level with very few translational changes. Finally, we show how mutations in transcriptional regulators lead to consistent and parallel changes in the expression levels of downstream genes. These results deepen our understanding of the molecular mechanisms underlying complex adaptations and provide insights into the repeatability of evolution.

Distinguishing Evolutionary Conservation from Derivedness

While the concept of “evolutionary conservation” has enabled biologists to explain many ancestral features and traits, it has also frequently been misused to evaluate the degree of changes from a common ancestor, or “derivedness”. We propose that the distinction of these two concepts allows us to properly understand phenotypic and organismal evolution. From a methodological aspect, “conservation” mainly considers genes or traits which species have in common, while “derivedness” additionally covers those that are not commonly shared, such as novel or lost traits and genes to evaluate changes from the time of divergence from a common ancestor. Due to these differences, while conservation-oriented methods are effective in identifying ancestral features, they may be prone to underestimating the overall changes accumulated during the evolution of certain lineages. Herein, we describe our recently developed method, “transcriptomic derivedness index”, for estimating the phenotypic derivedness of embryos with a molecular approach using the whole-embryonic transcriptome as a phenotype. Although echinoderms are often considered as highly derived species, our analyses with this method showed that their embryos, at least at the transcriptomic level, may not be much more derived than those of chordates. We anticipate that the future development of derivedness-oriented methods could provide quantitative indicators for finding highly/lowly evolvable traits.

Exploring the Genomic Patterns in Human and Mouse Cerebellums Via Single-Cell Sequencing and Machine Learning Method

In mammals, the cerebellum plays an important role in movement control. Cellular research reveals that the cerebellum involves a variety of sub-cell types, including Golgi, granule, interneuron, and unipolar brush cells. The functional characteristics of cerebellar cells exhibit considerable differences among diverse mammalian species, reflecting a potential development and evolution of nervous system. In this study, we aimed to recognize the transcriptional differences between human and mouse cerebellum in four cerebellar sub-cell types by using single-cell sequencing data and machine learning methods. A total of 321,387 single-cell sequencing data were used. The 321,387 cells included 4 cell types, i.e., Golgi (5,048, 1.57%), granule (250,307, 77.88%), interneuron (60,526, 18.83%), and unipolar brush (5,506, 1.72%) cells. Our results showed that by using gene expression profiles as features, the optimal classification model could achieve very high even perfect performance for Golgi, granule, interneuron, and unipolar brush cells, respectively, suggesting a remarkable difference between the genomic profiles of human and mouse. Furthermore, a group of related genes and rules contributing to the classification was identified, which might provide helpful information for deepening the understanding of cerebellar cell heterogeneity and evolution.

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.

A spatiotemporal atlas of the lepidopteran pest Helicoverpa armigera midgut provides insights into nutrient processing and pH regulation

Background

Caterpillars from the insect order Lepidoptera are some of the most widespread and destructive agricultural pests. Most of their impact is at the larval stage, where the midgut epithelium mediates the digestion and absorption of an astonishing amount of food. Although this tissue has been the subject of frequent investigation in Lepidoptera, a comprehensive expression atlas has yet to be generated.

Results

Here, we perform RNA-sequencing and proteomics on the gut of the polyphagous pest Helicoverpa armigera across, life stages, diet types, and compartments of the anterior-posterior axis. A striking relationship between the structural homology and expression pattern of a group of sugar transporters was observed in the early larval stages. Further comparisons were made among the spatial compartments of the midgut, which suggested a putative role for vATPases and SLC9 transporters in the generation of alkaline conditions in the H. armigera midgut.

Conclusions

This comprehensive resource will aid the scientific community in understanding lepidopteran gut physiology in unprecedented resolution. It is hoped that this study advances the understanding of the lepidopteran midgut and also facilitates functional work in this field.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-08274-x.

Lifestyle Evolution Analysis by Binary-State Speciation and Extinction (BiSSE) Model

Phylogenetic comparative methods (PCMs) combine statistics and evolutionary models to infer the dynamics of trait evolution and diversification that underlie the observed phylogeny. While PCMs have been used to study macro-evolutionary processes and evolutionary transitions of macroorganisms, their application to microbes is still limited. With the abundance of publicly available genomic and trait character data for diverse microbes nowadays, applications of PCMs on these data can provide insights into the fundamental principles that govern microbial evolution. Here, we introduce the Binary-State Speciation and Extinction (BiSSE) model, which is a relatively simple yet powerful approach for analyzing trait evolution. We begin by explaining the theoretical background and intuition behind the BiSSE model. Then, R commands for running the BiSSE model are presented. Finally, we introduce a case study that successfully applied the BiSSE model to investigate generalist and specialist microbial lifestyle evolution.

Special Care Is Needed in Applying Phylogenetic Comparative Methods to Gene Trees with Speciation and Duplication Nodes

How gene function evolves is a central question of evolutionary biology. It can be investigated by comparing functional genomics results between species and between genes. Most comparative studies of functional genomics have used pairwise comparisons. Yet it has been shown that this can provide biased results, as genes, like species, are phylogenetically related. Phylogenetic comparative methods should be used to correct for this, but they depend on strong assumptions, including unbiased tree estimates relative to the hypothesis being tested. Such methods have recently been used to test the “ortholog conjecture,” the hypothesis that functional evolution is faster in paralogs than in orthologs. Although pairwise comparisons of tissue specificity (τ) provided support for the ortholog conjecture, phylogenetic independent contrasts did not. Our reanalysis on the same gene trees identified problems with the time calibration of duplication nodes. We find that the gene trees used suffer from important biases, due to the inclusion of trees with no duplication nodes, to the relative age of speciations and duplications, to systematic differences in branch lengths, and to non-Brownian motion of tissue specificity on many trees. We find that incorrect implementation of phylogenetic method in empirical gene trees with duplications can be problematic. Controlling for biases allows successful use of phylogenetic methods to study the evolution of gene function and provides some support for the ortholog conjecture using three different phylogenetic approaches.

eVITTA: a web-based visualization and inference toolbox for transcriptome analysis

Transcriptome profiling is essential for gene regulation studies in development and disease. Current web-based tools enable functional characterization of transcriptome data, but most are restricted to applying gene-list-based methods to single datasets, inefficient in leveraging up-to-date and species-specific information, and limited in their visualization options. Additionally, there is no systematic way to explore data stored in the largest transcriptome repository, NCBI GEO. To fill these gaps, we have developed eVITTA (easy Visualization and Inference Toolbox for Transcriptome Analysis; https://tau.cmmt.ubc.ca/eVITTA/). eVITTA provides modules for analysis and exploration of studies published in NCBI GEO (easyGEO), detailed molecular- and systems-level functional profiling (easyGSEA), and customizable comparisons among experimental groups (easyVizR). We tested eVITTA on transcriptomes of SARS-CoV-2 infected human nasopharyngeal swab samples, and identified a downregulation of olfactory signal transducers, in line with the clinical presentation of anosmia in COVID-19 patients. We also analyzed transcriptomes of Caenorhabditis elegans worms with disrupted S-adenosylmethionine metabolism, confirming activation of innate immune responses and feedback induction of one-carbon cycle genes. Collectively, eVITTA streamlines complex computational workflows into an accessible interface, thus filling the gap of an end-to-end platform capable of capturing both broad and granular changes in human and model organism transcriptomes.