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Despite significant clinical progress in cell and gene therapies, maximizing protein expression in order to enhance potency remains a major technical challenge. Here, we develop a high-throughput strategy to design, screen, and optimize 5′ UTRs that enhance protein expression from a strong human cytomegalovirus (CMV) promoter. We first identify naturally occurring 5′ UTRs with high translation efficiencies and use this information with in silico genetic algorithms to generate synthetic 5′ UTRs. A total of ~12,000 5′ UTRs are then screened using a recombinase-mediated integration strategy that greatly enhances the sensitivity of high-throughput screens by eliminating copy number and position effects that limit lentiviral approaches. Using this approach, we identify three synthetic 5′ UTRs that outperform commonly used non-viral gene therapy plasmids in expressing protein payloads. In summary, we demonstrate that high-throughput screening of 5′ UTR libraries with recombinase-mediated integration can identify genetic elements that enhance protein expression, which should have numerous applications for engineered cell and gene therapies. The engineering of 5′ UTRs that modulate protein expression remains a great challenge. Here we leverage synthetic biology and computational design to develop a high-throughput strategy to design, screen, and optimize 5′ UTRs that enhance protein expression for non-viral gene therapies.

Abstract Our episodic memories vary in their specificity, ranging from a mere sense of familiarity to detailed recollection of the initial experience. Recent work suggests that alpha/beta desynchronization promotes information flow through the cortex, tracking the richness in detail of recovered memory representations. At the same time, as we age, memories become less vivid and detailed, which may be reflected in age-related reductions in alpha/beta desynchronization during retrieval. To understand age differences in the specificity of episodic memories, we investigated differences in alpha/beta desynchronization between younger (18–26 years, n = 31) and older (65–76 years, n = 27) adults during item recognition and lure discrimination. Alpha/beta desynchronization increased linearly with the demand for memory specificity, i.e., the requirement to retrieve details for an accurate response, across retrieval situations (correct rejections < item recognition < lure discrimination). Stronger alpha/beta desynchronization was related to memory success, as indicated by reliable activation differences between correct and incorrect memory responses. In line with the assumption of a loss of mnemonic detail in older age, older adults had more difficulties than younger adults to discriminate lures from targets. Importantly, they also showed a reduced modulation of alpha/beta desynchronization across retrieval demands. Together, these results extend previous findings by demonstrating that alpha/beta desynchronization dissociates between item recognition and the retrieval of highly detailed memories as required in lure discrimination, and that age-related impairments in episodic retrieval are accompanied by attenuated modulations in the alpha/beta band. Thus, we provide novel findings suggesting that alpha/beta desynchronization tracks mnemonic specificity and that changes in these oscillatory mechanisms may underlie age-related declines in episodic memory. Competing Interest Statement The authors have declared no competing interest. Footnotes * Declarations of interest: None.

The human brain’s transcriptome undergoes substantial changes over the lifespan and shows characteristic patterns that reflect anatomical regions and cellular compositions. In this thesis, I applied combinations of network algorithms and tools from computational biology to analyse transcriptional networks and their key regulators in the human brain across space (brain regions) and time (the human life course). First, I identified an age-dependent transcriptional network enriched for microglial markers. The microglia network recapitulated haematopoietic master regulators that are crucial for early microglia development using data from the ageing human brain only. In the second project, I demonstrated that gene clusters linked to neurogenesis during fetal life show moderate to strong preservation in the human adult brain. In addition to temporal development, I analysed transcriptional network dynamics across the spatial axis and detected a network of ion channel/transporter genes that express their longest 3’ untranslated regions (3’UTRs) exclusively in the brain. Enrichment for predicted miRNA response elements that are often shared among the ion channel/transporter genes, along with increased co-expression of this gene set, indicated that the extended 3’UTRs could serve as a hub for an endogenous competitive RNA network. I extended the analysis to differentiate between brain regions and additional regulatory RNA elements, namely long noncoding (lnc) RNAs. I found that genes in the hypothalamus express a region-specific network to which are also associated co-expressed lncRNAs. Finally, I added global metrics to the analysis of local networks. The dynamics of global network metrics indicated strong coordination of expression across the lifespan compared to similar variance within age groups. This work shows that the transcriptome of human post-mortem brains at least partially preserves the network structure of cell types and functionally related genes, and how it may be dissected using suitable combinations of bioinformatic algorithms.

Despite significant clinical progress in cell and gene therapies, maximizing protein expression in order to enhance potency remains a major challenge. One approach to increase protein expression is by optimizing translation through the engineering of 5′ untranslated regions (5′ UTRs). Here, we developed a high-throughput strategy to design, screen, and optimize novel 5′UTRs that enhance protein expression from a strong human cytomegalovirus (CMV) promoter. We first identified naturally occurring 5′ UTRs with high translation efficiencies and used this information with in silico genetic algorithms to generate synthetic 5′ UTRs. A total of ~12,000 5′ UTRs were then screened using a recombinase-mediated integration strategy that greatly enhances the sensitivity of high-throughput screens by eliminating copy number and position effects that limit lentiviral approaches. Using this approach, we identified three synthetic 5′ UTRs that outperformed commonly used non-viral gene therapy plasmids in expressing protein payloads. Furthermore, combinatorial assembly of these 5′ UTRs enabled even higher protein expression than obtained with each individual 5′ UTR. In summary, we demonstrate that high-throughput screening of 5′ UTR libraries with recombinase-mediated integration can identify genetic elements that enhance protein expression, which should have numerous applications for engineered cell and gene therapies.