Enhancer AAV toolbox for accessing and perturbing striatal cell types and circuits.

We present an enhancer AAV toolbox for accessing and perturbing striatal cell types and circuits. Best-in-class vectors were curated for accessing major striatal neuron populations including medium spiny neurons (MSNs), direct and indirect pathway MSNs, as well as Sst-Chodl, Pvalb-Pthlh, and cholinergic interneurons. Specificity was evaluated by multiple modes of molecular validation, three different routes of virus delivery, and with diverse transgene cargos.

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.

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.

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.

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.

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.

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 -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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Response to Hron et al.

Hron et al. provide transcriptome evidence that three (1.1 %) of the 274 genes reported by Lovell et al.

Conserved syntenic clusters of protein coding genes are missing in birds.

Background: Birds are one of the most highly successful and diverse groups of vertebrates, having evolved a number of distinct characteristics, including feathers and wings, a sturdy lightweight skeleton and unique respiratory and urinary/excretion systems. However, the genetic basis of these traits is poorly understood.

Results: Using comparative genomics based on extensive searches of 60 avian genomes, we have found that birds lack approximately 274 protein coding genes that are present in the genomes of most vertebrate lineages and are for the most part organized in conserved syntenic clusters in non-avian sauropsids and in humans.

Convergent transcriptional specializations in the brains of humans and song-learning birds.

Song-learning birds and humans share independently evolved similarities in brain pathways for vocal learning that are essential for song and speech and are not found in most other species. Comparisons of brain transcriptomes of song-learning birds and humans relative to vocal nonlearners identified convergent gene expression specializations in specific song and speech brain regions of avian vocal learners and humans. The strongest shared profiles relate bird motor and striatal song-learning nuclei, respectively, with human laryngeal motor cortex and parts of the striatum that control speech production and learning.