Tuning Expression Levels with Gene Regulatory Elements
By: Eric Griffith, PhD, Director of Research Development, Greenberg Lab, Department of Neurobiology at Harvard Medical School; Scientific Consultant, Apertura Gene Therapy
Developing gene therapies is a complex process, but the impact these medicines can have on the lives of patients is enormous.
Apertura is combining two technology platforms to fine-tune the delivery and expression of genetic medicines. For a gene therapy to be effective, it must be efficiently delivered to the target cell type(s), and once it’s there, it must express the therapeutic payload at an appropriate level while minimizing unwanted payload expression in off-target cell types.
For viral vector gene therapies, the design of the capsid generally determines efficient delivery to the target cell type, while the design of the nucleic acid-encoded payload determines efficient expression. Short genetic elements – broadly termed gene regulatory elements (GREs) -drive expression of the therapeutic payload at the designated levels in targeted cell types.
Eric Griffith, PhD
Why is Novel GRE Research Important?
Many genetic diseases involve either very high or low genetic expression, necessitating discrete control over gene therapy payload expression. Identifying novel GREs is critical for developing the next wave of genetic medicines and is a strategy that can be applied to a wide range of indications.
Newly engineered GREs are important because safely treating numerous indications requires that the therapeutic payload be expressed only in the target cell type with an exquisite level of precision. For example, with a channelopathy like Dravet syndrome, a gene therapy would need to express the SCN1A gene only in inhibitory neurons. Another example is Rett syndrome, a genetic disease caused by loss-of-function mutations in the MECP2 gene. That said, a two-fold overexpression of MECP2 also gives rise to a comparable disorder. Therefore, it’s crucial to have the ability to tightly control payload expression levels as well as regulate expression over time.
Promoters, GREs located directly upstream of the transcribed gene, have historically been the primary means of shaping payload expression. However, another type of GRE, short, gene-distal DNA sequences (~100-600 bp) called enhancers, also efficiently drive gene expression in a distance- and orientation-independent manner. Importantly, we now appreciate that enhancers are the primary mediators of cell- and tissue type-restricted gene transcription. With recent advances allowing us to identify enhancers at scale, we have discovered that there are more than 1 million enhancers in the human genome. The challenge is how to select and evaluate enhancers at scale to identify the best GREs for genetic medicines targeting specific cell types or tissues.
Apertura is leveraging tools such as the Paralleled Enhancer Single Cell Assay (PESCA) platform — which has been licensed from Michael Greenberg’s lab in the Department of Neurobiology at Harvard Medical School, where I am a Director of Research Development — to screen novel enhancers and other GREs at scale for tailored applications in gene therapy.
Identifying Novel GREs with PESCA
Traditionally, GREs have been evaluated in relatively small batches using imaging-based methods that visualize the expression of fluorescent reporter payloads driven by the different candidate GREs, but with millions of combinations it is not feasible to test these one at a time. The beauty of PESCA, as we described in 20191, is that we take advantage of the power of DNA barcoding technology, together with single-cell sequencing methods, to simultaneously assess hundreds to thousands of candidate GREs in the target tissue(s).
To achieve this, we first start with a defined need or goal – such as expression in a specific cell type of a tissue while not expressing in adjacent related cells. This allows us to focus our efforts on computationally evaluating the tremendous amount of data that is generated both in house and is available in the public domain, looking across the genome in a host of different cell types for marks of putatively active candidate GREs. Several hundred of the most promising GRE candidates are then cloned in parallel into gene therapy vectors, pairing each GRE with a unique barcode.
The resulting pooled viral library can be administered and screened in vivo in a single experiment. The distinct barcoded payloads will only be expressed in cells where the corresponding GRE was active. Single-cell analysis methods then allow us to determine the activity of each candidate GRE simultaneously across all the cell types present in the target tissue(s), enabling us to identify the GREs that drive our desired expression profile.
There has been a lot of work in the field to obtain novel GREs by screening large numbers of synthetic sequences in a given cell type in vitro. However, as components of gene therapeutics, GREs must function properly in the context of complex tissues, driving appropriate levels of payload expression in the target cell types while also minimizing expression in off-target cell populations. With PESCA, we can conduct our screens in vivo while still evaluating their activity across the full range of relevant cell types, taking advantage of the work nature has already done in crafting cell-type-selective GREs as jumping off points for further optimization both in vivo and in vitro.
Innovating Inside and Outside the Capsid
Apertura is unique in its combination of engineering novel GREs with PESCA and designing novel adeno-associated virus (AAV) capsids, optimizing delivery and expression of gene therapies. By pairing GRE engineering with the AAV capsid engineering platforms, we’re innovating inside and outside the capsid in our quest to expand the range of diseases that can be treated with genetic medicines – to ultimately reach more patients in need.
Contact Apertura to learn more about GRE engineering and opportunities to work together.
1 Hrvatin S et al (2019) A scalable platform for the development of cell-type-specific viral drivers eLife 8:e48089. https://doi.org/10.7554/eLife.48089