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1. Forebrain nuclei linked to woodpecker territorial drum displays mirror those that enable vocal learning in songbirds

   https://doi.org/10.1371/journal.pbio.3001751  

   Schuppe, Eric R. ; Cantin, Lindsey ; Chakraborty, Mukta ; Biegler, Matthew T. ; Jarvis, Electra R. ; Chen, Chun-Chun ; Hara, Erina ; Bertelsen, Mads F. ; Witt, Christopher C. ; Jarvis, Erich D. ; et al ( September 2022 , PLOS Biology)

   Townsend, Simon W. (Ed.)

   Vocal learning is thought to have evolved in 3 orders of birds (songbirds, parrots, and hummingbirds), with each showing similar brain regions that have comparable gene expression specializations relative to the surrounding forebrain motor circuitry. Here, we searched for signatures of these same gene expression specializations in previously uncharacterized brains of 7 assumed vocal non-learning bird lineages across the early branches of the avian family tree. Our findings using a conserved marker for the song system found little evidence of specializations in these taxa, except for woodpeckers. Instead, woodpeckers possessed forebrain regions that were anatomically similar to the pallial song nuclei of vocal learning birds. Field studies of free-living downy woodpeckers revealed that these brain nuclei showed increased expression of immediate early genes (IEGs) when males produce their iconic drum displays, the elaborate bill-hammering behavior that individuals use to compete for territories, much like birdsong. However, these specialized areas did not show increased IEG expression with vocalization or flight. We further confirmed that other woodpecker species contain these brain nuclei, suggesting that these brain regions are a common feature of the woodpecker brain. We therefore hypothesize that ancient forebrain nuclei for refined motor control may have given rise to not only the song control systems of vocal learning birds, but also the drumming system of woodpeckers. 

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

   https://doi.org/10.1126/science.abm7993  

   Kaplow, Irene M. ; Lawler, Alyssa J. ; Schäffer, Daniel E. ; Srinivasan, Chaitanya ; Sestili, Heather H. ; Wirthlin, Morgan E. ; Phan, BaDoi N. ; Prasad, Kavya ; Brown, Ashley R. ; Zhang, Xiaomeng ; et al ( April 2023 , Science)

   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] 

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3. Zebrafish gonad mutant models reveal neuroendocrine mechanisms of brain sexual dimorphism and male mating behaviors of different brain regions

   https://doi.org/10.1186/s13293-023-00534-7  

   Dai, Xiangyan ; Pradhan, Ajay ; Liu, Jiao ; Liu, Ruolan ; Zhai, Gang ; Zhou, Linyan ; Dai, Jiyan ; Shao, Feng ; Yuan, Zhiyong ; Wang, Zhijian ; et al ( August 2023 , Biology of Sex Differences)

   Sexually dimorphic mating behaviors differ between sexes and involve gonadal hormones and possibly sexually dimorphic gene expression in the brain. However, the associations among the brain, gonad, and sexual behavior in teleosts are still unclear. Here, we utilized germ cells-freetdrd12knockout (KO) zebrafish, and steroid synthesis enzymecyp17a1-deficient zebrafish to investigate the differences and interplays in the brain–gonad–behavior axis, and the molecular control of brain dimorphism and male mating behaviors.

   Tdrd12^+/−;cyp17a1^+/−double heterozygous parents were crossed to obtaintdrd12^−/−;cyp17a1^+/+(tdrd12 KO),tdrd12^+/+;cyp17a1^−/−(cyp17a1 KO), andtdrd12^−/−;cyp17a1^−/−(double KO) homozygous progenies. Comparative analysis of mating behaviors were evaluated using Viewpoint zebrafish tracking software and sexual traits were thoroughly characterized based on anatomical and histological experiments in these KOs and wild types. The steroid hormone levels (testosterone, 11-ketotestosterone and 17β-estradiol) in the brains, gonads, and serum were measured using ELISA kits. To achieve a higher resolution view of the differences in region-specific expression patterns of the brain, the brains of these KOs, and control male and female fish were dissected into three regions: the forebrain, midbrain, and hindbrain for transcriptomic analysis.

   Qualitative analysis of mating behaviors demonstrated thattdrd12^−/−fish behaved in the same manner as wild-type males to trigger oviposition behavior, whilecyp17a1^−/−and double knockout (KO) fish did not exhibit these behaviors. Based on the observation of sex characteristics, mating behaviors and hormone levels in these mutants, we found that the maintenance of secondary sex characteristics and male mating behavior did not depend on the presence of germ cells; rather, they depended mainly on the 11-ketotestosterone and testosterone levels secreted into the brain–gonad regulatory axis. RNA-seq analysis of different brain regions revealed that the brain transcript profile oftdrd12^−/−fish was similar to that of wild-type males, especially in the forebrain and midbrain. However, the brain transcript profiles ofcyp17a1^−/−and double KO fish were distinct from those of wild-type males and were partially biased towards the expression pattern of the female brain. Our results revealed important candidate genes and signaling pathways, such as synaptic signaling/neurotransmission, MAPK signaling, and steroid hormone pathways, that shape brain dimorphism and modulate male mating behavior in zebrafish.

   Our results provide comprehensive analyses and new insights regarding the endogenous interactions in the brain–gonad–behavior axis. Moreover, this study revealed the crucial candidate genes and neural signaling pathways of different brain regions that are involved in modulating brain dimorphism and male mating behavior in zebrafish, which would significantly light up the understanding the neuroendocrine and molecular mechanisms modulating brain dimorphism and male mating behavior in zebrafish and other teleost fish.

    

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4. Neurogenomic landscape associated with status‐dependent cooperative behaviour

   https://doi.org/10.1111/mec.17327  

   Bolton, Peri E. ; Ryder, T. Brandt ; Dakin, Roslyn ; Houtz, Jennifer L. ; Moore, Ignacio T. ; Balakrishnan, Christopher N. ; Horton, Brent M. ( March 2024 , Molecular Ecology)

   The neurogenomic mechanisms mediating male–male reproductive cooperative behaviours remain unknown. We leveraged extensive transcriptomic and behavioural data on a neotropical bird species (Pipra filicauda) that performs cooperative courtship displays to understand these mechanisms. In this species, the cooperative display is modulated by testosterone, which promotes cooperation in non‐territorial birds, but suppresses cooperation in territory holders. We sought to understand the neurogenomic underpinnings of three related traits: social status, cooperative display behaviour and testosterone phenotype. To do this, we profiled gene expression in 10 brain nuclei spanning the social decision‐making network (SDMN), and two key endocrine tissues that regulate social behaviour. We associated gene expression with each bird's behavioural and endocrine profile derived from 3 years of repeated measures taken from free‐living birds in the Ecuadorian Amazon. We found distinct landscapes of constitutive gene expression were associated with social status, testosterone phenotype and cooperation, reflecting the modular organization and engagement of neuroendocrine tissues. Sex‐steroid and neuropeptide signalling appeared to be important in mediating status‐specific relationships between testosterone and cooperation, suggesting shared regulatory mechanisms with male aggressive and sexual behaviours. We also identified differentially regulated genes involved in cellular activity and synaptic potentiation, suggesting multiple mechanisms underpin these genomic states. Finally, we identified SDMN‐wide gene expression differences between territorial and floater males that could form the basis of ‘status‐specific’ neurophysiological phenotypes, potentially mediated by testosterone and growth hormone. Overall, our findings provide new, systems‐level insights into the mechanisms of cooperative behaviour and suggest that differences in neurogenomic state are the basis for individual differences in social behaviour.

    

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5. Midbrain node for context-specific vocalisation in fish

   https://doi.org/10.1038/s41467-023-43794-y  

   Schuppe, Eric R. ; Ballagh, Irene ; Akbari, Najva ; Fang, Wenxuan ; Perelmuter, Jonathan T. ; Radtke, Caleb H. ; Marchaterre, Margaret A. ; Bass, Andrew H. ( January 2024 , Nature Communications)

   Vocalizations communicate information indicative of behavioural state across divergent social contexts. Yet, how brain regions actively pattern the acoustic features of context-specific vocal signals remains largely unexplored. The midbrain periaqueductal gray (PAG) is a major site for initiating vocalization among mammals, including primates. We show that PAG neurons in a highly vocal fish species (Porichthys notatus) are activated in distinct patterns during agonistic versus courtship calling by males, with few co-activated during a non-vocal behaviour, foraging. Pharmacological manipulations within vocally active PAG, but not hindbrain, sites evoke vocal network output to sonic muscles matching the temporal features of courtship and agonistic calls, showing that a balance of inhibitory and excitatory dynamics is likely necessary for patterning different call types. Collectively, these findings support the hypothesis that vocal species of fish and mammals share functionally comparable PAG nodes that in some species can influence the acoustic structure of social context-specific vocal signals.

    

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