A human-specific enhancer fine-tunes radial glia potency and corticogenesis.

Humans evolved an extraordinarily expanded and complex cerebral cortex, associated with developmental and gene regulatory modifications 1-3 . Human accelerated regions (HARs) are highly conserved genomic sequences with human-specific nucleotide substitutions. Although there are thousands of annotated HARs, their functional contribution to human-specific cortical development is largely unknown 4,5 . HARE5 is a HAR transcriptional enhancer of the WNT signaling receptor Frizzled8 (FZD8) active during brain development 6 . Here, using genome-edited mouse and primate models, we demonstrate that human (Hs) HARE5 fine-tunes cortical development and connectivity by controlling the proliferative and neurogenic capacity of neural progenitor cells (NPCs). Hs-HARE5 knock-in mice have significantly enlarged neocortices containing more neurons. By measuring neural dynamics in vivo we show these anatomical features correlate with increased functional independence between cortical regions. To understand the underlying developmental mechanisms, we assess progenitor fate using live imaging, lineage analysis, and single-cell RNA sequencing. This reveals Hs-HARE5 modifies radial glial progenitor behavior, with increased self-renewal at early developmental stages followed by expanded neurogenic potential. We use genome-edited human and chimpanzee (Pt) NPCs and cortical organoids to assess the relative enhancer activity and function of Hs-HARE5 and Pt-HARE5. Using these orthogonal strategies we show four human-specific variants in HARE5 drive increased enhancer activity which promotes progenitor proliferation. These findings illustrate how small changes in regulatory DNA can directly impact critical signaling pathways and brain development. Our study uncovers new functions for HARs as key regulatory elements crucial for the expansion and complexity of the human cerebral cortex.

The adaptor protein 2 (AP2) complex modulates habituation and behavioral selection across multiple pathways and time windows.

Animals constantly integrate sensory information with prior experience to select behavioral responses appropriate to the current situation. Genetic factors supporting this behavioral flexibility are often disrupted in neuropsychiatric conditions, such as the autism-linked ap2s1 gene which supports acoustically evoked habituation learning. ap2s1 encodes an AP2 endocytosis adaptor complex subunit, although its behavioral mechanisms and importance have been unclear. Here, we show that multiple AP2 subunits regulate acoustically evoked behavior selection and habituation learning in zebrafish. Furthermore, ap2s1 biases escape behavior choice in sensory modality-specific manners, and broadly regulates action selection across sensory contexts. We demonstrate that the AP2 complex functions acutely in the nervous system to modulate acoustically evoked habituation, suggesting several spatially and/or temporally distinct mechanisms through which AP2 regulates escape behavior selection and performance. Altogether, we show the AP2 complex coordinates action selection across diverse contexts, providing a vertebrate model for ap2s1's role in human conditions including autism spectrum disorder.

Human-specific features and developmental dynamics of the brain N-glycome.

Comparative "omics" studies have revealed unique aspects of human neurobiology, yet an evolutionary perspective of the brain N-glycome is lacking. We performed multiregional characterization of rat, macaque, chimpanzee, and human brain N-glycomes using chromatography and mass spectrometry and then integrated these data with complementary glycotranscriptomic data. We found that, in primates, the brain N-glycome has diverged more rapidly than the underlying transcriptomic framework, providing a means for rapidly generating additional interspecies diversity. Our data suggest that brain N-glycome evolution in hominids has been characterized by an overall increase in complexity coupled with a shift toward increased usage of α(2-6)-linked N-acetylneuraminic acid. Moreover, interspecies differences in the cell type expression pattern of key glycogenes were identified, including some human-specific differences, which may underpin this evolutionary divergence. Last, by comparing the prenatal and adult human brain N-glycomes, we uncovered region-specific neurodevelopmental pathways that lead to distinct spatial N-glycosylation profiles in the mature brain.

Utilizing novel diversity estimators to quantify multiple dimensions of microbial biodiversity across domains.

BACKGROUND: Microbial ecologists often employ methods from classical community ecology to analyze microbial community diversity. However, these methods have limitations because microbial communities differ from macro-organismal communities in key ways. This study sought to quantify microbial diversity using methods that are better suited for data spanning multiple domains of life and dimensions of diversity. Diversity profiles are one novel, promising way to analyze microbial datasets. Diversity profiles encompass many other indices, provide effective numbers of diversity (mathematical generalizations of previous indices that better convey the magnitude of differences in diversity), and can incorporate taxa similarity information. To explore whether these profiles change interpretations of microbial datasets, diversity profiles were calculated for four microbial datasets from different environments spanning all domains of life as well as viruses. Both similarity-based profiles that incorporated phylogenetic relatedness and naïve (not similarity-based) profiles were calculated. Simulated datasets were used to examine the robustness of diversity profiles to varying phylogenetic topology and community composition. RESULTS: Diversity profiles provided insights into microbial datasets that were not detectable with classical univariate diversity metrics. For all datasets analyzed, there were key distinctions between calculations that incorporated phylogenetic diversity as a measure of taxa similarity and naïve calculations. The profiles also provided information about the effects of rare species on diversity calculations. Additionally, diversity profiles were used to examine thousands of simulated microbial communities, showing that similarity-based and naïve diversity profiles only agreed approximately 50% of the time in their classification of which sample was most diverse. This is a strong argument for incorporating similarity information and calculating diversity with a range of emphases on rare and abundant species when quantifying microbial community diversity. CONCLUSIONS: For many datasets, diversity profiles provided a different view of microbial community diversity compared to analyses that did not take into account taxa similarity information, effective diversity, or multiple diversity metrics. These findings are a valuable contribution to data analysis methodology in microbial ecology.