While human genetic diseases can be caused by point mutations, insertions/deletions, chromosomal translocations or copy number variations, point mutations account for 58% of the pathogenic genetic variants causing disease. Programmable nucleases such as CRISPR-Cas are powerful tools but their use for the correction of point mutations in vivo poses some major challenges, namely, their reliance on the inefficient process of homologous recombination, threat of introducing permanent off-target mutations in the genome and immunogenicity due to their prokaryotic origin. In this dissertation, we develop and characterize an RNA editing toolset of human origin for correction of guanosine-to-adenosine mutations and premature stop codons. We engineer guide RNA to recruit exogenously expressed human adenosine deaminase acting on RNA (ADAR) enzymes to target transcripts and catalyze adenosine-to-inosine (guanosine) modifications. In a proof-of-concept study, we repair disease-causing premature stop codons and splice-site mutations in mouse models of Duchenne muscular dystrophy (DMD) and ornithine transcarbamylase (OTC) deficiency respectively, via exogenously delivered ADARs and associated guide RNA. However, exogenous delivery of ADARs leads to transcriptome-wide off-targeting, and additionally, enzymatic activity on certain RNA motifs, such as adenosines flanked by a 5’ guanosine is very low, thus limiting their utility as a transcriptome engineering toolset. To solve these issues, we develop a split-ADAR system with highly improved specificity profiles and also carry out a high throughput mutagenesis screen, identifying ADAR variants with enhanced activity at adenosines flanked by a 5’ guanosine. From a gene therapy perspective, recruitment of endogenous ADAR enzymes for editing a desired transcript creates minimal perturbation for the target cells as compared to exogenously delivered ADARs. Thus, we go on to engineer novel circular guide RNAs to recruit endogenous ADAR enzymes. We demonstrate its therapeutic potential by correcting a premature stop codon in a mouse model of Hurler syndrome via delivery of only circular guide RNA. Since immunogenicity against the delivery vehicle also limits efficacy of gene therapies, we develop a programmable adeno-associated virus (AAV) for gene delivery while also modifying it to evade neutralization by pre-existing antibodies in the serum.

Compared to modern supercomputers, which consume roughly 10^6 W of power, the human brain requires only 20 W to function, and still exceeds the performance of supercomputers in many creative tasks. This stark difference in energy requirements is caused by a fundamental difference in computing architecture. Modern computers follow the Von Neumann architecture, in which transistors dedicated to logic and memory functions are physically separated, and the time and energy required to communicate between the two units constitutes a bottleneck which impedes performance in machine learning and optimization problems. On the other hand, in the human brain, logic, memory, and learning functions are integrated together in a single element: the synapse. Without the Von Neumann bottleneck, the brain can achieve fast real-time learning, adaptability in complex environments, and massive parallelism. For the future of neuromorphic computing, it is important to develop an electronic device which mimics the synaptic function, so that large-scale circuits which mimic the neurobiological architecture can be developed. This work reports a silicon-based synaptic resistor (referred to as “synstor” hereinafter) which integrates logic, learning, and memory in a single device. The synstor is composed of a semiconducting silicon channel connected via Schottky contacts to titanium input and output electrodes, a thermal silicon dioxide, an aluminum oxide switching layer, and a tantalum oxide reference electrode. The large defect density in the switching layer attracts or repels charge carriers in the silicon channel to modify its conductance, and the defect density in turn can be modified by voltage pulses applied on its input and output electrodes (pre- and post-synaptic spikes). Synaptic resistor circuits could be scaled-up to facilitate mobile artificial intelligence systems with brain-like intelligence and adaptability in complex environments.

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