Sleeping Beauty mRNA-LNP enables stable rAAV transgene expression in mouse and NHP hepatocytes and improves vector potency.

Recombinant adeno-associated virus (rAAV) vector gene delivery systems have demonstrated great promise in clinical trials but continue to face durability and dose-related challenges. Unlike rAAV gene therapy, integrating gene addition approaches can provide curative expression in mitotically active cells and pediatric populations. We explored a novel in vivo delivery approach based on an engineered transposase, Sleeping Beauty (SB100X), delivered as an mRNA within a lipid nanoparticle (LNP), in combination with an rAAV-delivered transposable transgene. This combinatorial approach achieved correction of ornithine transcarbamylase deficiency in the neonatal Spfash mouse model following a single delivery to dividing hepatocytes in the newborn liver. Correction remained stable into adulthood, while a conventional rAAV approach resulted in a return to the disease state. In non-human primates, integration by transposition, mediated by this technology, improved gene expression 10-fold over conventional rAAV-mediated gene transfer while requiring 5-fold less vector. Additionally, integration site analysis confirmed a random profile while specifically targeting TA dinucleotides across the genome. Together, these findings demonstrate that transposable elements can improve rAAV-delivered therapies by lowering the vector dose requirement and associated toxicity while expanding target cell types.

Discovering homotypic binding events at high spatial resolution.

MOTIVATION: Clusters of protein-DNA interaction events involving the same transcription factor are known to act as key components of invertebrate and mammalian promoters and enhancers. However, detecting closely spaced homotypic events from ChIP-Seq data is challenging because random variation in the ChIP fragmentation process obscures event locations. RESULTS: The Genome Positioning System (GPS) can predict protein-DNA interaction events at high spatial resolution from ChIP-Seq data, while retaining the ability to resolve closely spaced events that appear as a single cluster of reads. GPS models observed reads using a complexity penalized mixture model and efficiently predicts event locations with a segmented EM algorithm. An optional mode permits GPS to align common events across distinct experiments. GPS detects more joint events in synthetic and actual ChIP-Seq data and has superior spatial resolution when compared with other methods. In addition, the specificity and sensitivity of GPS are superior to or comparable with other methods. AVAILABILITY: http://cgs.csail.mit.edu/gps.

Genome-Scale CRISPR Screens Identify Human Pluripotency-Specific Genes.

Human pluripotent stem cells (hPSCs) generate a variety of disease-relevant cells that can be used to improve the translation of preclinical research. Despite the potential of hPSCs, their use for genetic screening has been limited by technical challenges. We developed a scalable and renewable Cas9 and sgRNA-hPSC library in which loss-of-function mutations can be induced at will. Our inducible mutant hPSC library can be used for multiple genome-wide CRISPR screens in a variety of hPSC-induced cell types. As proof of concept, we performed three screens for regulators of properties fundamental to hPSCs: their ability to self-renew and/or survive (fitness), their inability to survive as single-cell clones, and their capacity to differentiate. We identified the majority of known genes and pathways involved in these processes, as well as a plethora of genes with unidentified roles. This resource will increase the understanding of human development and genetics. This approach will be a powerful tool to identify disease-modifying genes and pathways.

Allele-specific epigenome maps reveal sequence-dependent stochastic switching at regulatory loci.

To assess the impact of genetic variation in regulatory loci on human health, we constructed a high-resolution map of allelic imbalances in DNA methylation, histone marks, and gene transcription in 71 epigenomes from 36 distinct cell and tissue types from 13 donors. Deep whole-genome bisulfite sequencing of 49 methylomes revealed sequence-dependent CpG methylation imbalances at thousands of heterozygous regulatory loci. Such loci are enriched for stochastic switching, which is defined as random transitions between fully methylated and unmethylated states of DNA. The methylation imbalances at thousands of loci are explainable by different relative frequencies of the methylated and unmethylated states for the two alleles. Further analyses provided a unifying model that links sequence-dependent allelic imbalances of the epigenome, stochastic switching at gene regulatory loci, and disease-associated genetic variation.