Phenotypic variation among species is a product of evolutionary changes to developmental programs1,2. However, how these changes generate novel morphological traits remains largely unclear. Here we studied the genomic and developmental basis of the mammalian gliding membrane, or patagium—an adaptative trait that has repeatedly evolved in different lineages, including in closely related marsupial species. Through comparative genomic analysis of 15 marsupial genomes, both from gliding and non-gliding species, we find that the
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Vocal production learning (“vocal learning”) is a convergently evolved trait in vertebrates. To identify brain genomic elements associated with mammalian vocal learning, we integrated genomic, anatomical, and neurophysiological data from the Egyptian fruit bat (
- NSF-PAR ID:
- 10532420
- Author(s) / Creator(s):
- ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; more »
- Publisher / Repository:
- Science
- Date Published:
- Journal Name:
- Science
- Volume:
- 383
- Issue:
- 6690
- ISSN:
- 0036-8075
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Emx2 locus experienced lineage-specific patterns of acceleratedcis -regulatory evolution in gliding species. By combining epigenomics, transcriptomics and in-pouch marsupial transgenics, we show thatEmx2 is a critical upstream regulator of patagium development. Moreover, we identify differentcis -regulatory elements that may be responsible for driving increasedEmx2 expression levels in gliding species. Lastly, using mouse functional experiments, we find evidence thatEmx2 expression patterns in gliders may have been modified from a pre-existing program found in all mammals. Together, our results suggest that patagia repeatedly originated through a process of convergent genomic evolution, whereby regulation ofEmx2 was altered by distinctcis -regulatory elements in independently evolved species. Thus, different regulatory elements targeting the same key developmental gene may constitute an effective strategy by which natural selection has harnessed regulatory evolution in marsupial genomes to generate phenotypic novelty. -
INTRODUCTION Diverse phenotypes, including large brains relative to body size, group living, and vocal learning ability, have evolved multiple times throughout mammalian history. These shared phenotypes may have arisen repeatedly by means of common mechanisms discernible through genome comparisons. RATIONALE Protein-coding sequence differences have failed to fully explain the evolution of multiple mammalian phenotypes. This suggests that these phenotypes have evolved at least in part through changes in gene expression, meaning that their differences across species may be caused by differences in genome sequence at enhancer regions that control gene expression in specific tissues and cell types. Yet the enhancers involved in phenotype evolution are largely unknown. Sequence conservation–based approaches for identifying such enhancers are limited because enhancer activity can be conserved even when the individual nucleotides within the sequence are poorly conserved. This is due to an overwhelming number of cases where nucleotides turn over at a high rate, but a similar combination of transcription factor binding sites and other sequence features can be maintained across millions of years of evolution, allowing the function of the enhancer to be conserved in a particular cell type or tissue. Experimentally measuring the function of orthologous enhancers across dozens of species is currently infeasible, but new machine learning methods make it possible to make reliable sequence-based predictions of enhancer function across species in specific tissues and cell types. RESULTS To overcome the limits of studying individual nucleotides, we developed the Tissue-Aware Conservation Inference Toolkit (TACIT). Rather than measuring the extent to which individual nucleotides are conserved across a region, TACIT uses machine learning to test whether the function of a given part of the genome is likely to be conserved. More specifically, convolutional neural networks learn the tissue- or cell type–specific regulatory code connecting genome sequence to enhancer activity using candidate enhancers identified from only a few species. This approach allows us to accurately associate differences between species in tissue or cell type–specific enhancer activity with genome sequence differences at enhancer orthologs. We then connect these predictions of enhancer function to phenotypes across hundreds of mammals in a way that accounts for species’ phylogenetic relatedness. We applied TACIT to identify candidate enhancers from motor cortex and parvalbumin neuron open chromatin data that are associated with brain size relative to body size, solitary living, and vocal learning across 222 mammals. Our results include the identification of multiple candidate enhancers associated with brain size relative to body size, several of which are located in linear or three-dimensional proximity to genes whose protein-coding mutations have been implicated in microcephaly or macrocephaly in humans. We also identified candidate enhancers associated with the evolution of solitary living near a gene implicated in separation anxiety and other enhancers associated with the evolution of vocal learning ability. We obtained distinct results for bulk motor cortex and parvalbumin neurons, demonstrating the value in applying TACIT to both bulk tissue and specific minority cell type populations. To facilitate future analyses of our results and applications of TACIT, we released predicted enhancer activity of >400,000 candidate enhancers in each of 222 mammals and their associations with the phenotypes we investigated. CONCLUSION TACIT leverages predicted enhancer activity conservation rather than nucleotide-level conservation to connect genetic sequence differences between species to phenotypes across large numbers of mammals. TACIT can be applied to any phenotype with enhancer activity data available from at least a few species in a relevant tissue or cell type and a whole-genome alignment available across dozens of species with substantial phenotypic variation. Although we developed TACIT for transcriptional enhancers, it could also be applied to genomic regions involved in other components of gene regulation, such as promoters and splicing enhancers and silencers. As the number of sequenced genomes grows, machine learning approaches such as TACIT have the potential to help make sense of how conservation of, or changes in, subtle genome patterns can help explain phenotype evolution. Tissue-Aware Conservation Inference Toolkit (TACIT) associates genetic differences between species with phenotypes. TACIT works by generating open chromatin data from a few species in a tissue related to a phenotype, using the sequences underlying open and closed chromatin regions to train a machine learning model for predicting tissue-specific open chromatin and associating open chromatin predictions across dozens of mammals with the phenotype. [Species silhouettes are from PhyloPic]more » « less
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Abstract For many animal species, vocal communication is a critical social behavior and often a necessary component of reproductive success. Additionally, vocalizations are often demanding motor acts. Wanting to know whether a specific molecular toolkit might be required for vocalization, we used RNA‐sequencing to investigate neural gene expression underlying the performance of an extreme vocal behavior, the courtship hum of the plainfin midshipman fish (
Porichthys notatus ). Single hums can last up to 2 h and may be repeated throughout an evening of courtship activity. We asked whether vocal behavioral states are associated with specific gene expression signatures in key brain regions that regulate vocalization by comparing transcript expression levels in humming versus non‐humming males. We find that the circadian‐related genesperiod3 andClock are significantly upregulated in the vocal motor nucleus and preoptic area‐anterior hypothalamus, respectively, in humming compared with non‐humming males, indicating that internal circadian clocks may differ between these divergent behavioral states. In addition, we identify suites of differentially expressed genes related to synaptic transmission, ion channels and transport, neuropeptide and hormone signaling, and metabolism and antioxidant activity that together may support the neural and energetic demands of humming behavior. Comparisons of transcript expression across regions stress regional differences in brain gene expression, while also showing coordinated gene regulation in the vocal motor circuit in preparation for courtship behavior. These results underscore the role of differential gene expression in shifts between behavioral states, in this case neuroendocrine, motor and circadian control of courtship vocalization. -
Bats are the second largest mammalian order, with over 1,300 species. These animals show diverse behaviors, diets, and habitats. Most bats produce ultrasonic vocalizations and perceive their environment by processing information carried by returning echoes of their calls. Echolocation is achieved through a sophisticated audio-vocal system that allows bats to emit and detect frequencies that can range from ten to hundreds of kilohertz. In addition, most bat species are gregarious, and produce social communication calls that vary in complexity, form, and function across species. In this article, we (a) highlight the value of bats as model species for research on social communication, (b) review behavioral and neurophysiological studies of bat acoustic communication signal production and processing, and (c) discuss important directions for future research in this field. We propose that comparative studies of bat acoustic communication can provide new insights into sound processing and vocal learning across the animal kingdom.more » « less
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Corresponding attributes of neural development and functionsuggest arthropod and vertebrate brains may have an evolutionarily conserved organization. However, the underlying mechanisms have remained elusive. Here, we identify a gene regulatory and character identity network defining the deutocerebral– tritocerebral boundary (DTB) in Drosophila. This network comprises genes homologous to those directing midbrain-hindbrainboundary (MHB) formation in vertebrates and their closest chordate relatives.Genetic tracing reveals that the embryonic DTB gives rise to adult midbrain circuits that in flies control auditory and vestibular information processing and motor coordination, as do MHB-derived circuits in vertebrates. DTB-specific gene expression and function are directed by cis-regulatory elements of developmental control genes that include homologs of mammalian Zinc finger of the cerebellum and Purkinje cell protein 4. Drosophila DTB-specific cis-regulatory elements correspond to regulatory sequences of human ENGRAILED-2, PAX-2, and DACHSHUND-1 that direct MHB-specific expression in the embryonic mouse brain. We show that cis-regulatory elements and the gene networks they regulate direct the formation and function of midbrain circuits for balance and motor coordination in insects and mammals. Regulatory mechanisms mediating the genetic specification of cephalic neural circuits in arthropods correspond to those in chordates, thereby implying their origin before the divergence of deuterostomes and ecdysozoans.more » « less
