Zinc finger nuclease gene repair as a treatment for cystinosis

Cystinosis is a multi-system autosomal recessive disorder caused by mutations and/or deletions in both alleles of CTNS, a gene encoding for the low pH dependent lysosomal cystine exporter cystinosin. Cystinosis occurs in approximately 1:200,000 newborns worldwide and is characterised by an accumulation of cystine in the lysosomes. The most severe form of the disorder is nephropathic cystinosis presenting Fanconi syndrome and leads without treatment to an end-stage renal failure before the age of ten. The only treatment available so far is cysteamine therapy, which delays disease progression by five years, but does not provide a cure for cystinosis patients. Current gene and cell based therapeutic approaches have not yet provided a suitable alternative. A potentially approach for a long-term treatment could be to generate autologous gene–modified stem cells by repairing the gene. Zinc Finger Nucleases (ZFNs) serve as a tool to increase HDR up to a 200,000-fold by introducing a double-stranded break (DSB). Thus, simple mutations in the CTNS gene could be corrected by introduction of a double-stranded break using ZFNs to boost the process of HDR with a suitable donor DNA sequence. A permanent repair of the most common lesion CTNS, a 57 kb deletion, could be achieved by ZFN-mediated HDR using a minigene CTNS promoter/cDNA construct. The thesis describes the design and testing of seven zinc finger nuclease pairs for their cleavage activity in vitro and in cellulo.. A highly sensitive assay to detect even low levels of ZFN-mediated HDR was also developed. Finally, to further investigate the role of autophagy in tissue injury in cystinotic cells an assay to monitor autophagy levels in the cells was successfully developed. This assay provides the opportunity to demonstrate functional restoration of CTNS after successful ZFN-HDR in cystinotic cells.

Neonatal EEG classification using atomic decomposition

The electroencephalogram (EEG) is an important noninvasive tool used in the neonatal intensive care unit (NICU) for the neurologic evaluation of the sick newborn infant. It provides an excellent assessment of at-risk newborns and formulates a prognosis for long-term neurologic outcome.The automated analysis of neonatal EEG data in the NICU can provide valuable information to the clinician facilitating medical intervention. The aim of this thesis is to develop a system for automatic classification of neonatal EEG which can be mainly divided into two parts: (1) classification of neonatal EEG seizure from nonseizure, and (2) classifying neonatal background EEG into several grades based on the severity of the injury using atomic decomposition. Atomic decomposition techniques use redundant time-frequency dictionaries for sparse signal representations or approximations. The first novel contribution of this thesis is the development of a novel time-frequency dictionary coherent with the neonatal EEG seizure states. This dictionary was able to track the time-varying nature of the EEG signal. It was shown that by using atomic decomposition and the proposed novel dictionary, the neonatal EEG transition from nonseizure to seizure states could be detected efficiently. The second novel contribution of this thesis is the development of a neonatal seizure detection algorithm using several time-frequency features from the proposed novel dictionary. It was shown that the time-frequency features obtained from the atoms in the novel dictionary improved the seizure detection accuracy when compared to that obtained from the raw EEG signal. With the assistance of a supervised multiclass SVM classifier and several timefrequency features, several methods to automatically grade EEG were explored. In summary, the novel techniques proposed in this thesis contribute to the application of advanced signal processing techniques for automatic assessment of neonatal EEG recordings.