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Nucleotide changes in gene regulatory elements are important determinants of neuronal development and diseases. Using massively parallel reporter assays in primary human cells from mid-gestation cortex and cerebral organoids, we interrogated the cis-regulatory activity of 102,767 open chromatin regions, including thousands of sequences with cell type-specific accessibility and variants associated with brain gene regulation. In primary cells, we identified 46,802 active enhancer sequences and 164 variants that alter enhancer activity. Activity was comparable in organoids and primary cells, suggesting that organoids provide an adequate model for the developing cortex. Using deep learning we decoded the sequence basis and upstream regulators of enhancer activity. This work establishes a comprehensive catalog of functional gene regulatory elements and variants in human neuronal development.

Adeno-associated viruses (AAVs) hold tremendous promise as delivery vectors for gene therapies. AAVs have been successfully engineered--for instance, for more efficient and/or cell-specific delivery to numerous tissues--by creating large, diverse starting libraries and selecting for desired properties. However, these starting libraries often contain a high proportion of variants unable to assemble or package their genomes, a prerequisite for any gene delivery goal. Here, we present and showcase a machine learning (ML) method for designing AAV peptide insertion libraries that achieve fivefold higher packaging fitness than the standard NNK library with negligible reduction in diversity. To demonstrate our ML-designed library's utility for downstream engineering goals, we show that it yields approximately 10-fold more successful variants than the NNK library after selection for infection of human brain tissue, leading to a promising glial-specific variant. Moreover, our design approach can be applied to other types of libraries for AAV and beyond. [ABSTRACT FROM AUTHOR]

Autism spectrum disorder (ASD) commonly co-occurs with congenital heart disease (CHD), but the molecular mechanisms underlying this comorbidity remain unknown. Given that children with CHD come to clinical attention by the newborn period, understanding which CHD variants carry ASD risk could provide an opportunity to identify and treat individuals at high risk for developing ASD far before the typical age of diagnosis. Therefore, it is critical to delineate the subset of CHD genes most likely to increase the risk of ASD. However, to date there is relatively limited overlap between high confidence ASD and CHD genes, suggesting that alternative strategies for prioritizing CHD genes are necessary. Recent studies have shown that ASD gene perturbations commonly dysregulate neural progenitor cell (NPC) biology. Thus, we hypothesized that CHD genes that disrupt neurogenesis are more likely to carry risk for ASD. Hence, we performed an in vitro pooled CRISPR interference (CRISPRi) screen to identify CHD genes that disrupt NPC biology similarly to ASD genes. Overall, we identified 45 CHD genes that strongly impact proliferation and/or survival of NPCs. Moreover, we observed that a cluster of physically interacting ASD and CHD genes are enriched for ciliary biology. Studying seven of these genes with evidence of shared risk ( CEP290, CHD4, KMT2E, NSD1, OFD1, RFX3, TAOK1 ), we observe that perturbation significantly impacts primary cilia formation in vitro . While in vivo investigation of TAOK1 reveals a previously unappreciated role for the gene in motile cilia formation and heart development, supporting its prediction as a CHD risk gene. Together, our findings highlight a set of CHD risk genes that may carry risk for ASD and underscore the role of cilia in shared ASD and CHD biology., Competing Interests: COMPETING INTERESTS The authors do not have any competing interests.

In recent years, we and others have identified a number of enhancers that, when incorporated into rAAV vectors, can restrict the transgene expression to particular neuronal populations. Yet, viral tools to access and manipulate fine neuronal subtypes are still limited. Here, we performed systematic analysis of single cell genomic data to identify enhancer candidates for each of the cortical interneuron subtypes. We established a set of enhancer-AAV tools that are highly specific for distinct cortical interneuron populations and striatal cholinergic neurons. These enhancers, when used in the context of different effectors, can target (fluorescent proteins), observe activity (GCaMP) and manipulate (opto- or chemo-genetics) specific neuronal subtypes. We also validated our enhancer-AAV tools across species. Thus, we provide the field with a powerful set of tools to study neural circuits and functions and to develop precise and targeted therapy., Competing Interests: G.F. is a founder of Regel Therapeutics, which has no competing interests with the present manuscript. G.F. is an advisor for Neuron and Annual Review of Neuroscience. J.D. and K.A. are employees of Regel Therapeutics and J.D. is also a founder. A.I. is the founder of Tibbling Technologies. Patents are pending on all enhancers present in this manuscript. For BiChate27 and BiSSTe10 G.F. and J.D hold this patent. For the remainder of enhancer patents they are held by G.F., M.D. and Y.W.

Adeno-associated viruses (AAVs) hold tremendous promise as delivery vectors for gene therapies. AAVs have been successfully engineered-for instance, for more efficient and/or cell-specific delivery to numerous tissues-by creating large, diverse starting libraries and selecting for desired properties. However, these starting libraries often contain a high proportion of variants unable to assemble or package their genomes, a prerequisite for any gene delivery goal. Here, we present and showcase a machine learning (ML) method for designing AAV peptide insertion libraries that achieve fivefold higher packaging fitness than the standard NNK library with negligible reduction in diversity. To demonstrate our ML-designed library's utility for downstream engineering goals, we show that it yields approximately 10-fold more successful variants than the NNK library after selection for infection of human brain tissue, leading to a promising glial-specific variant. Moreover, our design approach can be applied to other types of libraries for AAV and beyond.