Browsing by Subject "Promoter engineering"
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Item Beyond protein factories : expanding the synthetic biology toolkit for engineering mammalian hosts(2017-01-20) Cheng, Joseph Kit; Alper, Hal S.; Contreras, Lydia M; Georgiou, George; Sullivan, Christopher SThe incredible clinical and commercial successes of recombinant protein therapeutics cemented the use of mammalian cells as the premier production hosts for these products. However, we can further exploit these cells to harness their potential for addressing current and future medical needs through metabolic and advanced engineering of these cells. To do so, we need a deeper understanding of the intricate gene regulation network that governs these cells and the ability to attain precise control of gene expression levels. In addition, some of these applications, such as gene therapy and immunotherapy, could benefit greatly by refraining from using viral-derived genetic elements. Therefore, this work seeks to establish additional transcriptional control elements to improve our ability to regulate expression with generalizable approaches and methods, facilitating the adaptation of these techniques for any mammalian cell type of interest. Here, we successfully demonstrated three key genetic elements can be utilized to tune gene expression in a rational manner. First, we conducted a genome-wide screen to survey genomic integration sites that support high transcriptional activity. We showed that CRISPR/Cas9-mediated de novo integration into one of these transcriptional hot-spots at the GRIK1 locus resulted in a 2.4-fold increase in heterologous gene expression over random integration. Subsequently, we set the groundwork necessary to evaluate a cell line development strategy that aims to increase the frequency of successful de novo targeted integrations. Second, we utilized two approaches for rational promoter engineering. We established a transcriptomics-guided workflow for de novo synthetic promoter design based on the Design-Build-Test paradigm. By using this workflow, we generated two synthetic designs that were comparable to a strong viral promoter and a strong endogenous promoter. We also employed an alternative approach by creating hybrid promoters, which resulted in a hybrid promoter variant that was also comparable to the same viral and endogenous promoters. Third, we exploited the general mammalian terminator structure and created a synthetic terminator that was comparable to a strong viral terminator. We evaluated 12 endogenous and 30 synthetic terminators for heterologous gene expression and revealed interactions between several key components of the terminator. Critically, we showed that transgene expression was 1.9x higher with endogenous and synthetic elements when compared with strong viral-derived elements. Ultimately, we showed that transgene expression can be finely adjusted by the approaches and methods described in this dissertation, and that viral-derived elements can be readily substituted by our synthetic designs.Item Harnessing Yarrowia lipolytica’s potential as a lipid and alkane production platform(2013-08) Blazeck, John James; Alper, Hal S.; Contreras, Lydia; Ellington, Andrew; Georgiou, George; Maynard, JenniferEngineering cellular phenotype can enable the in vivo synthesis of renewable fuels, industrial precursors, and pharmaceuticals. Achieving economic viability requires the use of a cellular platform that generates high titers independent of fermentation condition, through either native or imported biosynthetic metabolism. While lacking fully developed genetic tools, the oleaginous yeast Yarrowia lipolytica has the native capacity to produce large titers of lipids and citric acid cycle intermediates. However, unlocking this biosynthetic capacity requires complete rewiring of native metabolism. To this end, this work focuses on the development and engineering of the yeast Y. lipolytica to rewire native metabolism and enable the production of lipids, alkanes, and itaconic acid. Precise control of gene expression is a requisite to enable metabolic and pathway engineering applications for any host organism. However, Y. lipolytica lacks promoter elements strong enough to manipulate intracellular metabolism. Thus, we utilized a hybrid promoter engineering approach to produce libraries of high-expressing, tunable promoters, seven-fold stronger than promoters previously characterized in Y. lipolytica 1,2. We successfully applied this approach to Saccharomyces cerevisiae, expanding transcriptional capacity of the strongest constitutive to highlight our hybrid approach as a generalizable method to increase expression capacity in eukaryotic organisms 3. We utilized our novel Y. lipolytica hybrid promoters to drive intracellular metabolism towards lipid production and to overexpress heterologous enzymes that enable alkane and itaconic acid production. Specifically, we implemented a global rewiring of Y. lipolytica’s native metabolism to increase lipogenesis more than sixty fold to 25.3g/L (the highest lipid production ever reported) and generated cells nearly 90% lipid content. We further expressed a lipoxygenase enzyme to catalyze the novel microbial production of the short-chain n-alkane, pentane. Finally, we exploited Y. lipolytica’s capacity to accumulate citric acid cycle intermediates by expressing a heterologous cis-aconitic acid decarboxylase enzyme to produce itaconic acid. Increasing substrate availability through media optimization and genomic engineering increased pentane and itaconic acid production threefold and eightfold, respectively 4. Collectively, these studies have facilitated the utilization of Y. lipolytica as an industrially relevant microbial platform, and represent a generic approach towards enabling biosynthetic control in microbial hosts will ill-defined gene expression technology.Item Promoter element engineering towards modulated and consistent gene expression(2023-04-20) Presnell, Kristin Virginia; Alper, Hal S.; Contreras, Lydia; Ren, Pengyu; Truskett, ThomasMetabolic engineering requires precise control over expression strength, which can be effectively accomplished through engineering of the promoter element. In this regard, there is a need in the field to develop synthetic promoters that function predictably and consistently. To enable success in these engineering endeavors, an understanding of the currently elusive sequence-to-function relationship of these promoter elements is needed. This work advances our understanding of promoter architecture and has developed several methods by which this understanding can be leveraged to create new synthetic promoters. Specifically, this work produces modular synthetic components of promoters that can be combined to function predictably and consistently in a variety of circumstances. First, we show that promoters in E. coli can be divided into two separable components, the core promoter and the upstream element (UP element), and through mutagenesis of the UP element region alone, we create a suite of new promoters capable of up to 9-fold activation of expression of a core promoter. Further, we showcase the modularity of these UP elements by placing them upstream of different core promoters and observing conserved levels of expression activation. Second, we show promoters in Saccharomyces cerevisiae can be engineered to perform consistently across exponential and stationary growth phase through a computational motif discovery approach. Here, we show that fragments of promoters containing these motifs from each growth phase can be combined to achieve a promoter with consistent and strong expression across both phases. We see a 38-fold increase in exponential phase signal when exponential motifs are inserted into a native promoter with high stationary phase expression. Further, we show this consistency is retained across multiple scales of growth by characterizing expression in microtiter plates, tubes, and flasks. Finally, we utilize an entirely computational approach to address challenges in computational classification of promoters in S. cerevisiae stemming from the length and complexity of these sequences. Deep learning algorithms capable of decoding higher order sequence patterns are only employed on small (<100 base pair) sequences, or datasets containing tens of thousands of examples. In this work, we show that inclusion of promoters from phylogenetically related species can improve upon the performance of convolutional neural nets trained to recognize promoting function in 800 base pair input sequences, compared to models trained on only the 6,000 available S. cerevisiae promoter sequences alone.