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Biology subjects

Scholes, C.

Publications and source records attributed to Scholes, C..

3 recordsLinked to original sources

Single-AAV CRISPR editing of skeletal muscle in non-human primates with NanoCas, an ultracompact nuclease

CRISPR gene editing is a transformative technology for addressing genetic diseases, but delivery constraints have largely limited its therapeutic applications to liver-targeted and ex vivo therapies. Here, we present the discovery and engineering of NanoCas, an ultracompact CRISPR nuclease capable of extending CRISPRs reach in vivo beyond liver targets. We experimentally screened 176 ultracompact CRISPR systems found in metagenomic data and applied protein engineering approaches to enhance the editing efficiency of NanoCas. The optimized NanoCas exhibits potent editing capabilities across various cell systems and tissues in vivo when administered via adeno-associated viral (AAV) vectors. This is accomplished despite NanoCas being approximately one-third the size of conventional CRISPR nucleases. In proof-of-concept experiments, we observed robust editing with our optimized NanoCas in mouse models targeting Pcsk9, a gene involved in cholesterol regulation, and targeting exon splice sites in dystrophin to address Duchenne muscular dystrophy (DMD) mutations. We further tested the efficacy of our NanoCas system in vivo in non-human primates (NHPs) resulting in editing levels above 30% in muscle tissues. The compact size of NanoCas, in combination with robust nuclease editing, opens the door for single-AAV editing of non-liver tissues in vivo, including the use of newer editing modalities such as reverse transcriptase (RT) editing, base editing, and epigenetic editing.

bioengineering↗

Transcriptional kinetic synergy: a complex landscape revealed by integrating modelling and synthetic biology

1Gene regulation involves synergistic interactions between transcription factors (TFs). Classical thermodynamic models offer a biophysical understanding of synergy based on binding cooperativity and regulated recruitment of RNA polymerase. However, transcription requires polymerase to transition through multiple states. Accordingly, recent work has suggested that "kinetic synergy" can arise through TFs differentially regulating distinct steps of the transcription cycle. Disentangling both sources of synergy has been challenging. Here, we combine theory and experiment to analyze TFs binding to a single shared site, thereby removing simultaneous specific DNA binding. Using the graph-based linear framework, we integrate TF binding with regulation of the transcription cycle, and reveal a complex kinetic synergy landscape dependent on TF concentration, DNA binding and transcriptional activity. We exploit synthetic zinc-finger TF fusions to experimentally interrogate these predictions. Our results confirm that transcription cycle regulation must be integrated with recruitment for a quantitative understanding of transcriptional control.

systems biology↗

Computations performed by shadow enhancers and enhancer duplications vary across the Drosophila embryo

Transcription of developmental genes is controlled by multiple enhancers. Frequently, more than one enhancer can activate transcription from the same promoter in the same cells. In these cases, how is regulatory information from multiple enhancers combined to determine the overall expression output of their shared promoter? To investigate this question, we quantified nascent transcription driven by a pair shadow enhancers, each individual of the pair, and their duplications in Drosophila embryos using live imaging. This set of constructs allows us to quantify the \"computation\" made by the pairs of enhancers: their combined output expression as a function of the expression that they drive separately. We show that the computation performed by these shadow enhancers and duplications varies across the expression pattern, implying that how their activities are combined depends on the transcriptional regulators bound to the enhancers in different parts of the embryo. Characterizing the computation made by multiple enhancers is a critical first step in developing conceptual and computational models of gene expression at the locus level, where multiple enhancers collaborate.

systems biology↗