Vocal learning-associated convergent evolution in mammalian proteins and regulatory elements.

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 (Rousettus aegyptiacus) with analyses of the genomes of 215 placental mammals. First, we identified a set of proteins evolving more slowly in vocal learners. Then, we discovered a vocal motor cortical region in the Egyptian fruit bat, an emergent vocal learner, and leveraged that knowledge to identify active cis-regulatory elements in the motor cortex of vocal learners. Machine learning methods applied to motor cortex open chromatin revealed 50 enhancers robustly associated with vocal learning whose activity tended to be lower in vocal learners. Our research implicates convergent losses of motor cortex regulatory elements in mammalian vocal learning evolution.

Evolutionary constraint and innovation across hundreds of placental mammals.

Zoonomia is the largest comparative genomics resource for mammals produced to date. By aligning genomes for 240 species, we identify bases that, when mutated, are likely to affect fitness and alter disease risk. At least 332 million bases (~10.7%) in the human genome are unusually conserved across species (evolutionarily constrained) relative to neutrally evolving repeats, and 4552 ultraconserved elements are nearly perfectly conserved. Of 101 million significantly constrained single bases, 80% are outside protein-coding exons and half have no functional annotations in the Encyclopedia of DNA Elements (ENCODE) resource. Changes in genes and regulatory elements are associated with exceptional mammalian traits, such as hibernation, that could inform therapeutic development. Earth's vast and imperiled biodiversity offers distinctive power for identifying genetic variants that affect genome function and organismal phenotypes.

Relating enhancer genetic variation across mammals to complex phenotypes using machine learning.

Protein-coding differences between species often fail to explain phenotypic diversity, suggesting the involvement of genomic elements that regulate gene expression such as enhancers. Identifying associations between enhancers and phenotypes is challenging because enhancer activity can be tissue-dependent and functionally conserved despite low sequence conservation. We developed the Tissue-Aware Conservation Inference Toolkit (TACIT) to associate candidate enhancers with species' phenotypes using predictions from machine learning models trained on specific tissues. Applying TACIT to associate motor cortex and parvalbumin-positive interneuron enhancers with neurological phenotypes revealed dozens of enhancer-phenotype associations, including brain size-associated enhancers that interact with genes implicated in microcephaly or macrocephaly. TACIT provides a foundation for identifying enhancers associated with the evolution of any convergently evolved phenotype in any large group of species with aligned genomes.

Systems biology as a framework to understand the physiological and endocrine bases of behavior and its evolution-From concepts to a case study in birds.

Organismal behavior, with its tremendous complexity and diversity, is generated by numerous physiological systems acting in coordination. Understanding how these systems evolve to support differences in behavior within and among species is a longstanding goal in biology that has captured the imagination of researchers who work on a multitude of taxa, including humans. Of particular importance are the physiological determinants of behavioral evolution, which are sometimes overlooked because we lack a robust conceptual framework to study mechanisms underlying adaptation and diversification of behavior. Here, we discuss a framework for such an analysis that applies a "systems view" to our understanding of behavioral control. This approach involves linking separate models that consider behavior and physiology as their own networks into a singular vertically integrated behavioral control system. In doing so, hormones commonly stand out as the links, or edges, among nodes within this system. To ground our discussion, we focus on studies of manakins (Pipridae), a family of Neotropical birds. These species have numerous physiological and endocrine specializations that support their elaborate reproductive displays. As a result, manakins provide a useful example to help imagine and visualize the way systems concepts can inform our appreciation of behavioral evolution. In particular, manakins help clarify how connectedness among physiological systems-which is maintained through endocrine signaling-potentiate and/or constrain the evolution of complex behavior to yield behavioral differences across taxa. Ultimately, we hope this review will continue to stimulate thought, discussion, and the emergence of research focused on integrated phenotypes in behavioral ecology and endocrinology.

Machine learning sequence prioritization for cell type-specific enhancer design.

Recent discoveries of extreme cellular diversity in the brain warrant rapid development of technologies to access specific cell populations within heterogeneous tissue. Available approaches for engineering-targeted technologies for new neuron subtypes are low yield, involving intensive transgenic strain or virus screening. Here, we present Specific Nuclear-Anchored Independent Labeling (SNAIL), an improved virus-based strategy for cell labeling and nuclear isolation from heterogeneous tissue. SNAIL works by leveraging machine learning and other computational approaches to identify DNA sequence features that confer cell type-specific gene activation and then make a probe that drives an affinity purification-compatible reporter gene. As a proof of concept, we designed and validated two novel SNAIL probes that target parvalbumin-expressing (PV+) neurons. Nuclear isolation using SNAIL in wild-type mice is sufficient to capture characteristic open chromatin features of PV+ neurons in the cortex, striatum, and external globus pallidus. The SNAIL framework also has high utility for multispecies cell probe engineering; expression from a mouse PV+ SNAIL enhancer sequence was enriched in PV+ neurons of the macaque cortex. Expansion of this technology has broad applications in cell type-specific observation, manipulation, and therapeutics across species and disease models.

Inferring mammalian tissue-specific regulatory conservation by predicting tissue-specific differences in open chromatin.

BACKGROUND: Evolutionary conservation is an invaluable tool for inferring functional significance in the genome, including regions that are crucial across many species and those that have undergone convergent evolution. Computational methods to test for sequence conservation are dominated by algorithms that examine the ability of one or more nucleotides to align across large evolutionary distances. While these nucleotide alignment-based approaches have proven powerful for protein-coding genes and some non-coding elements, they fail to capture conservation of many enhancers, distal regulatory elements that control spatial and temporal patterns of gene expression. The function of enhancers is governed by a complex, often tissue- and cell type-specific code that links combinations of transcription factor binding sites and other regulation-related sequence patterns to regulatory activity. Thus, function of orthologous enhancer regions can be conserved across large evolutionary distances, even when nucleotide turnover is high. RESULTS: We present a new machine learning-based approach for evaluating enhancer conservation that leverages the combinatorial sequence code of enhancer activity rather than relying on the alignment of individual nucleotides. We first train a convolutional neural network model that can predict tissue-specific open chromatin, a proxy for enhancer activity, across mammals. Next, we apply that model to distinguish instances where the genome sequence would predict conserved function versus a loss of regulatory activity in that tissue. We present criteria for systematically evaluating model performance for this task and use them to demonstrate that our models accurately predict tissue-specific conservation and divergence in open chromatin between primate and rodent species, vastly out-performing leading nucleotide alignment-based approaches. We then apply our models to predict open chromatin at orthologs of brain and liver open chromatin regions across hundreds of mammals and find that brain enhancers associated with neuron activity have a stronger tendency than the general population to have predicted lineage-specific open chromatin. CONCLUSION: The framework presented here provides a mechanism to annotate tissue-specific regulatory function across hundreds of genomes and to study enhancer evolution using predicted regulatory differences rather than nucleotide-level conservation measurements.

Transcriptional and anatomical diversity of medium spiny neurons in the primate striatum.

Medium spiny neurons (MSNs) constitute the vast majority of striatal neurons and the principal interface between dopamine reward signals and functionally diverse cortico-basal ganglia circuits. Information processing in these circuits is dependent on distinct MSN types: cell types that are traditionally defined according to their projection targets or dopamine receptor expression. Single-cell transcriptional studies have revealed greater MSN heterogeneity than predicted by traditional circuit models, but the transcriptional landscape in the primate striatum remains unknown. Here, we set out to establish molecular definitions for MSN subtypes in Rhesus monkeys and to explore the relationships between transcriptionally defined subtypes and anatomical subdivisions of the striatum. Our results suggest at least nine MSN subtypes, including dorsal striatum subtypes associated with striosome and matrix compartments, ventral striatum subtypes associated with the nucleus accumbens shell and olfactory tubercle, and an MSN-like cell type restricted to µ-opioid receptor rich islands in the ventral striatum. Although each subtype was demarcated by discontinuities in gene expression, continuous variation within subtypes defined gradients corresponding to anatomical locations and, potentially, functional specializations. These results lay the foundation for achieving cell-type-specific transgenesis in the primate striatum and provide a blueprint for investigating circuit-specific information processing.

Addiction-Associated Genetic Variants Implicate Brain Cell Type- and Region-Specific Cis-Regulatory Elements in Addiction Neurobiology.

Recent large genome-wide association studies have identified multiple confident risk loci linked to addiction-associated behavioral traits. Most genetic variants linked to addiction-associated traits lie in noncoding regions of the genome, likely disrupting cis-regulatory element (CRE) function. CREs tend to be highly cell type-specific and may contribute to the functional development of the neural circuits underlying addiction. Yet, a systematic approach for predicting the impact of risk variants on the CREs of specific cell populations is lacking. To dissect the cell types and brain regions underlying addiction-associated traits, we applied stratified linkage disequilibrium score regression to compare genome-wide association studies to genomic regions collected from human and mouse assays for open chromatin, which is associated with CRE activity. We found enrichment of addiction-associated variants in putative CREs marked by open chromatin in neuronal (NeuN+) nuclei collected from multiple prefrontal cortical areas and striatal regions known to play major roles in reward and addiction. To further dissect the cell type-specific basis of addiction-associated traits, we also identified enrichments in human orthologs of open chromatin regions of female and male mouse neuronal subtypes: cortical excitatory, D1, D2, and PV. Last, we developed machine learning models to predict mouse cell type-specific open chromatin, enabling us to further categorize human NeuN+ open chromatin regions into cortical excitatory or striatal D1 and D2 neurons and predict the functional impact of addiction-associated genetic variants. Our results suggest that different neuronal subtypes within the reward system play distinct roles in the variety of traits that contribute to addiction.SIGNIFICANCE STATEMENT We combine statistical genetic and machine learning techniques to find that the predisposition to for nicotine, alcohol, and cannabis use behaviors can be partially explained by genetic variants in conserved regulatory elements within specific brain regions and neuronal subtypes of the reward system. Our computational framework can flexibly integrate open chromatin data across species to screen for putative causal variants in a cell type- and tissue-specific manner for numerous complex traits.

Molecular specializations of deep cortical layer analogs in songbirds.

How the evolution of complex behavioral traits is associated with the emergence of novel brain pathways is largely unknown. Songbirds, like humans, learn vocalizations via tutor imitation and possess a specialized brain circuitry to support this behavior. In a comprehensive in situ hybridization effort, we show that the zebra finch vocal robust nucleus of the arcopallium (RA) shares numerous markers (e.g. SNCA, PVALB) with the adjacent dorsal intermediate arcopallium (AId), an avian analog of mammalian deep cortical layers with involvement in motor function. We also identify markers truly unique to RA and thus likely linked to modulation of vocal motor function (e.g. KCNC1, GABRE), including a subset of the known shared markers between RA and human laryngeal motor cortex (e.g. SLIT1, RTN4R, LINGO1, PLXNC1). The data provide novel insights into molecular features unique to vocal learning circuits, and lend support for the motor theory for vocal learning origin.

HALPER facilitates the identification of regulatory element orthologs across species.

SUMMARY: Diverse traits have evolved through cis-regulatory changes in genome sequence that influence the magnitude, timing and cell type-specificity of gene expression. Advances in high-throughput sequencing and regulatory genomics have led to the identification of regulatory elements in individual species, but these genomic regions remain difficult to align across taxonomic orders due to their lack of sequence conservation relative to protein coding genes. The groundwork for tracing the evolution of regulatory elements is provided by the recent assembly of hundreds of genomes, the generation of reference-free Cactus multiple sequence alignments of these genomes, and the development of the halLiftover tool for mapping regions across these alignments. We present halLiftover Post-processing for the Evolution of Regulatory Elements (HALPER), a tool for constructing contiguous regulatory element orthologs from the outputs of halLiftover. We anticipate that this tool will enable users to efficiently identify orthologs of regulatory elements across hundreds of species, providing novel insights into the evolution of traits that have evolved through gene expression. AVAILABILITY AND IMPLEMENTATION: HALPER is implemented in python and available on github: https://github.com/pfenninglab/halLiftover-postprocessing. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

ZEBrA: Zebra finch Expression Brain Atlas-A resource for comparative molecular neuroanatomy and brain evolution studies.

An in-depth understanding of the genetics and evolution of brain function and behavior requires a detailed mapping of gene expression in functional brain circuits across major vertebrate clades. Here we present the Zebra finch Expression Brain Atlas (ZEBrA; www.zebrafinchatlas.org, RRID: SCR_012988), a web-based resource that maps the expression of genes linked to a broad range of functions onto the brain of zebra finches. ZEBrA is a first of its kind gene expression brain atlas for a bird species and a first for any sauropsid. ZEBrA's >3,200 high-resolution digital images of in situ hybridized sections for ~650 genes (as of June 2019) are presented in alignment with an annotated histological atlas and can be browsed down to cellular resolution. An extensive relational database connects expression patterns to information about gene function, mouse expression patterns and phenotypes, and gene involvement in human diseases and communication disorders. By enabling brain-wide gene expression assessments in a bird, ZEBrA provides important substrates for comparative neuroanatomy and molecular brain evolution studies. ZEBrA also provides unique opportunities for linking genetic pathways to vocal learning and motor control circuits, as well as for novel insights into the molecular basis of sex steroids actions, brain dimorphisms, reproductive and social behaviors, sleep function, and adult neurogenesis, among many fundamental themes.

A Modular Approach to Vocal Learning: Disentangling the Diversity of a Complex Behavioral Trait.

Vocal learning is a behavioral trait in which the social and acoustic environment shapes the vocal repertoire of individuals. Over the past century, the study of vocal learning has progressed at the intersection of ecology, physiology, neuroscience, molecular biology, genomics, and evolution. Yet, despite the complexity of this trait, vocal learning is frequently described as a binary trait, with species being classified as either vocal learners or vocal non-learners. As a result, studies have largely focused on a handful of species for which strong evidence for vocal learning exists. Recent studies, however, suggest a continuum in vocal learning capacity across taxa. Here, we further suggest that vocal learning is a multi-component behavioral phenotype comprised of distinct yet interconnected modules. Discretizing the vocal learning phenotype into its constituent modules would facilitate integration of findings across a wider diversity of species, taking advantage of the ways in which each excels in a particular module, or in a specific combination of features. Such comparative studies can improve understanding of the mechanisms and evolutionary origins of vocal learning. We propose an initial set of vocal learning modules supported by behavioral and neurobiological data and highlight the need for diversifying the field in order to disentangle the complexity of the vocal learning phenotype.

Molecular architecture of the zebra finch arcopallium.

The arcopallium, a key avian forebrain region, receives inputs from numerous brain areas and is a major source of descending sensory and motor projections. While there is evidence of arcopallial subdivisions, the internal organization or the arcopallium is not well understood. The arcopallium is also considered the avian homologue of mammalian deep cortical layers and/or amygdalar subdivisions, but one-to-one correspondences are controversial. Here we present a molecular characterization of the arcopallium in the zebra finch, a passerine songbird species and a major model organism for vocal learning studies. Based on in situ hybridization for arcopallial-expressed transcripts (AQP1, C1QL3, CBLN2, CNTN4, CYP19A1, ESR1/2, FEZF2, MGP, NECAB2, PCP4, PVALB, SCN3B, SCUBE1, ZBTB20, and others) in comparison with cytoarchitectonic features, we have defined 20 distinct regions that can be grouped into six major domains (anterior, posterior, dorsal, ventral, medial, and intermediate arcopallium, respectively; AA, AP, AD, AV, AM, and AI). The data also help to establish the arcopallium as primarily pallial, support a unique topography of the arcopallium in passerines, highlight similarities between the vocal robust nucleus of the arcopallium (RA) and AI, and provide insights into the similarities and differences of cortical and amygdalar regions between birds and mammals. We also propose the use of AMV (instead of nucleus taenia/TnA), AMD, AD, and AI as initial steps toward a universal arcopallial nomenclature. Besides clarifying the internal organization of the arcopallium, the data provide a coherent basis for further functional and comparative studies of this complex avian brain region.

Parrot Genomes and the Evolution of Heightened Longevity and Cognition.

Parrots are one of the most distinct and intriguing groups of birds, with highly expanded brains [1], highly developed cognitive [2] and vocal communication [3] skills, and a long lifespan compared to other similar-sized birds [4]. Yet the genetic basis of these traits remains largely unidentified. To address this question, we have generated a high-coverage, annotated assembly of the genome of the blue-fronted Amazon (Amazona aestiva) and carried out extensive comparative analyses with 30 other avian species, including 4 additional parrots. We identified several genomic features unique to parrots, including parrot-specific novel genes and parrot-specific modifications to coding and regulatory sequences of existing genes. We also discovered genomic features under strong selection in parrots and other long-lived birds, including genes previously associated with lifespan determination as well as several hundred new candidate genes. These genes support a range of cellular functions, including telomerase activity; DNA damage repair; control of cell proliferation, cancer, and immunity; and anti-oxidative mechanisms. We also identified brain-expressed, parrot-specific paralogs with known functions in neural development or vocal-learning brain circuits. Intriguingly, parrot-specific changes in conserved regulatory sequences were overwhelmingly associated with genes that are linked to cognitive abilities and have undergone similar selection in the human lineage, suggesting convergent evolution. These findings bring novel insights into the genetics and evolution of longevity and cognition, as well as provide novel targets for exploring the mechanistic basis of these traits.

Curation of microarray oligonucleotides and corresponding ESTs/cDNAs used for gene expression analysis in zebra finches.

OBJECTIVES: Zebra finches are a major model organism for investigating mechanisms of vocal learning, a trait that enables spoken language in humans. The development of cDNA collections with expressed sequence tags (ESTs) and microarrays has allowed for extensive molecular characterizations of circuitry underlying vocal learning and production. However, poor database curation can lead to errors in transcriptome and bioinformatics analyses, limiting the impact of these resources. Here we used genomic alignments and synteny analysis for orthology verification to curate and reannotate ~ 35% of the oligonucleotides and corresponding ESTs/cDNAs that make-up Agilent microarrays for gene expression analysis in finches. DATA DESCRIPTION: We found that: (1) 5475 out of 43,084 oligos (a) failed to align to the zebra finch genome, (b) aligned to multiple loci, or (c) aligned to Chr_un only, and thus need to be flagged until a better genome assembly is available, or (d) reflect cloning artifacts; (2) Out of 9635 valid oligos examined further, 3120 were incorrectly named, including 1533 with no known orthologs; and (3) 2635 oligos required name update. The resulting curated dataset provides a reference for correcting gene identification errors in previous finch microarrays studies, and avoiding such errors in future studies.

The constitutive differential transcriptome of a brain circuit for vocal learning.

BACKGROUND: The ability to imitate the vocalizations of other organisms, a trait known as vocal learning, is shared by only a few organisms, including humans, where it subserves the acquisition of speech and language, and 3 groups of birds. In songbirds, vocal learning requires the coordinated activity of a set of specialized brain nuclei referred to as the song control system. Recent efforts have revealed some of the genes that are expressed in these vocal nuclei, however a thorough characterization of the transcriptional specializations of this system is still missing. We conducted a rigorous and comprehensive analysis of microarrays, and conducted a separate analysis of 380 genes by in situ hybridizations in order to identify molecular specializations of the major nuclei of the song system of zebra finches (Taeniopygia guttata), a songbird species. RESULTS: Our efforts identified more than 3300 genes that are differentially regulated in one or more vocal nuclei of adult male birds compared to the adjacent brain regions. Bioinformatics analyses provided insights into the possible involvement of these genes in molecular pathways such as cellular morphogenesis, intrinsic cellular excitability, neurotransmission and neuromodulation, axonal guidance and cela-to-cell interactions, and cell survival, which are known to strongly influence the functional properties of the song system. Moreover, an in-depth analysis of specific gene families with known involvement in regulating the development and physiological properties of neuronal circuits provides further insights into possible modulators of the song system. CONCLUSION: Our study represents one of the most comprehensive molecular characterizations of a brain circuit that evolved to facilitate a learned behavior in a vertebrate. The data provide novel insights into possible molecular determinants of the functional properties of the song control circuitry. It also provides lists of compelling targets for pharmacological and genetic manipulations to elucidate the molecular regulation of song behavior and vocal learning.

A New Chicken Genome Assembly Provides Insight into Avian Genome Structure.

The importance of the Gallus gallus (chicken) as a model organism and agricultural animal merits a continuation of sequence assembly improvement efforts. We present a new version of the chicken genome assembly (Gallus_gallus-5.0; GCA_000002315.3), built from combined long single molecule sequencing technology, finished BACs, and improved physical maps. In overall assembled bases, we see a gain of 183 Mb, including 16.4 Mb in placed chromosomes with a corresponding gain in the percentage of intact repeat elements characterized. Of the 1.21 Gb genome, we include three previously missing autosomes, GGA30, 31, and 33, and improve sequence contig length 10-fold over the previous Gallus_gallus-4.0. Despite the significant base representation improvements made, 138 Mb of sequence is not yet located to chromosomes. When annotated for gene content, Gallus_gallus-5.0 shows an increase of 4679 annotated genes (2768 noncoding and 1911 protein-coding) over those in Gallus_gallus-4.0. We also revisited the question of what genes are missing in the avian lineage, as assessed by the highest quality avian genome assembly to date, and found that a large fraction of the original set of missing genes are still absent in sequenced bird species. Finally, our new data support a detailed map of MHC-B, encompassing two segments: one with a highly stable gene copy number and another in which the gene copy number is highly variable. The chicken model has been a critical resource for many other fields of study, and this new reference assembly will substantially further these efforts.

A putative RA-like region in the brain of the scale-backed antbird, Willisornis poecilinotus (Furnariides, Suboscines, Passeriformes, Thamnophilidae).

The memorization and production of song in songbirds share important parallels with the process of speech acquisition in humans. In songbirds, these processes are dependent on a group of specialized telencephalic nuclei known as the song system: HVC (used as a proper name), RA (robust nucleus of arcopallium), LMAN (lateral magnocellular nucleus of the nidopallium) and striatal Area X. A recent study suggested that the arcopallium of the Sayornis phoebe, a non vocal learner suboscine species, contains a nucleus with some properties similar to those of songbird RA, suggesting that the song system may have been present in the last common ancestor of these groups. Here we report morphological and gene expression evidence that a region with some properties similar to RA is present in another suboscine, the Amazonian endemic Willisornis poecilinotus. Specifically, a discrete domain with a distinct Nissl staining pattern and that expresses the RA marker RGS4 was found in the arcopallium where the oscine RA is localized. Our findings, combined with the previous report on the S. phoebe, suggest that an arcopallial region with some RA-like properties was present in the ancestor of both Suboscines infraorders Tyranni and Furnarii, and is possibly an ancestral feature of Passeriformes.

Response to Hron et al.

Hron et al. provide transcriptome evidence that three (1.1 %) of the 274 genes reported by Lovell et al. as missing in birds may actually be 'hidden' as a result of high GC content. Although this factor may explain some gene absences from genomic assemblies, we believe it is insufficient to account for the extensive syntenic losses described in Lovell et al. Please see related article: www.dx.doi.org/10.1186/s13059-015-0724-z.

Whole-genome analyses resolve early branches in the tree of life of modern birds.

To better determine the history of modern birds, we performed a genome-scale phylogenetic analysis of 48 species representing all orders of Neoaves using phylogenomic methods created to handle genome-scale data. We recovered a highly resolved tree that confirms previously controversial sister or close relationships. We identified the first divergence in Neoaves, two groups we named Passerea and Columbea, representing independent lineages of diverse and convergently evolved land and water bird species. Among Passerea, we infer the common ancestor of core landbirds to have been an apex predator and confirm independent gains of vocal learning. Among Columbea, we identify pigeons and flamingoes as belonging to sister clades. Even with whole genomes, some of the earliest branches in Neoaves proved challenging to resolve, which was best explained by massive protein-coding sequence convergence and high levels of incomplete lineage sorting that occurred during a rapid radiation after the Cretaceous-Paleogene mass extinction event about 66 million years ago.