Search

Abstract Exon skipping technologies enable exclusion of targeted exons from mature mRNA transcripts, which have broad applications in medicine and biotechnology. Existing techniques including antisense oligonucleotides, targetable nucleases, and base editors, while effective for specific applications, remain hindered by transient effects, genotoxicity, and inconsistent exon skipping. To overcome these limitations, here we develop SPLICER, a toolbox of next-generation base editors containing near-PAMless Cas9 nickase variants fused to adenosine or cytosine deaminases for the simultaneous editing of splice acceptor (SA) and splice donor (SD) sequences. Synchronized SA and SD editing improves exon skipping, reduces aberrant splicing, and enables skipping of exons refractory to single splice site editing. To demonstrate the therapeutic potential of SPLICER, we target APP exon 17, which encodes amino acids that are cleaved to form Aβ plaques in Alzheimer’s disease. SPLICER reduces the formation of Aβ42 peptides in vitro and enables efficient exon skipping in a mouse model of Alzheimer’s disease. Overall, SPLICER is a widely applicable and efficient exon skipping toolbox.

It is widely assumed that in our lifetimes the products available in the global economy have become more diverse. This assumption is difficult to investigate directly, however, because it is difficult to collect the necessary data about every product in an economy each year. We solve this problem by mining publicly available textual descriptions of the products of every large US firms each year from 1997 to 2017. Although many aspects of economic productivity have been steadily rising during this period, our text-based measurements show that the diversity of the products of at least large US firms has steadily declined. This downward trend is visible using a variety of product diversity metrics, including some that depend on a measurement of the similarity of the products of every single pair of firms. The current state of the art in comprehensive and detailed firm-similarity measurements is a Boolean word vector model due to Hoberg and Phillips. We measure diversity using firm-similarities from this Boolean model and two more sophisticated variants, and we consistently observe a significant dropping trend in product diversity. These results make it possible to frame and start to test specific hypotheses for explaining the dropping product diversity trend.

It is widely assumed that in our lifetimes the products available in the global economy have become more diverse. This assumption is difficult to investigate directly, however, because it is difficult to collect the necessary data about every product in an economy each year. We solve this problem by mining publicly available textual descriptions of the products of every large US firms each year from 1997 to 2017. Although many aspects of economic productivity have been steadily rising during this period, our text-based measurements show that the diversity of the products of at least large US firms has steadily declined. This downward trend is visible using a variety of product diversity metrics, including some that depend on a measurement of the similarity of the products of every single pair of firms. The current state of the art in comprehensive and detailed firm-similarity measurements is a Boolean word vector model due to Hoberg and Phillips. We measure diversity using firm-similarities from this Boolean model and two more sophisticated variants, and we consistently observe a significant dropping trend in product diversity. These results make it possible to frame and start to test specific hypotheses for explaining the dropping product diversity trend.