The promoter is one of several regulatory elements that determine how a gene is expressed. 5′ and 3′ untranslated regions, gene repressors and insulators are examples of other non-coding sequences that contribute to expression level, mRNA stability and tissue specificity. On top of this, capsid engineering is a completely independent approach to determin cell-type specificity. For programs where fine-tuning the full expression vector matters, Annogen’s SuRE™ platform can be applied to screen and optimize a broad range of these elements, going beyond what promoter engineering alone can achieve.
Most expression engineering focuses on the promoter, and for good reason: it is the primary switch. But the promoter sets transcription in the context of the rest of the cassette. The 5′ untranslated region (UTR), together with the Kozak context, shapes how efficiently the transcript is translated. The 3′ UTR and the polyadenylation (polyA) signal influence transcript stability and half-life. Introns can raise expression through splicing-coupled export. Insulators buffer a cassette against the influence of its genomic neighborhood, and silencers can actively dampen expression in off-target cells. Each of these elements is non-coding, hard to predict from sequence alone, and measurable at scale.
SuRE™ applies the same massively parallel reporter assay (MPRA) principle we use for promoters to these elements. We build libraries in which each candidate is tracked by dozens to hundreds of barcodes, express them in the relevant cell type and vector context, and read out their effect on the transcript. Because the readout is redundant and quantitative, we can separate small, real differences from screening noise. Where the property that matters acts at the level of translation rather than transcription, for example a 5′ UTR that changes ribosome loading without changing mRNA level, we use a protein-level readout instead of, or alongside, the RNA-level one.
Match UTRs to a target expression level and transcript-stability profile within a DNA-encoded cassette.
Compare polyadenylation signals for transcript stability and read-through.
Assess the contribution of introns and post-transcriptional regulatory elements where the vector’s packaging capacity allows.
Identify elements that shield expression from genomic position effects, or that actively silence in off-target cell types, using the same counter-screening logic we apply to promoter specificity.
Combine the best promoter with matched UTR, intron and polyA choices in a single screen, bounded by your vector’s packaging limit rather than trimmed after the fact.
The capsid is not a regulatory element: it is what gets your cassette into the cell, and it governs delivery efficiency and tropism rather than transcription. It does, however, share one property with our promoter work, in that capsid variants are best compared empirically, at scale, with a barcoded readout. The same massively parallel, barcode-tracked approach that underpins SuRE™ can be extended to capsid or serotype variant libraries, where the measured property is transduction and tropism rather than transcriptional output. If delivery and tissue targeting sit on the critical path for your program alongside expression, this is worth scoping with us directly.
Define the expression behavior the cassette needs: target cell type, expression level, off-target constraints and any size limit your vector imposes.
Design a library of candidate elements (UTRs, introns, polyA signals, insulators, silencers), synthetic or genome-derived, with your preferred references spiked in for direct comparison.
Screen with SuRE™ in the relevant cell type and vector context, using an RNA-level or protein-level readout as the biology requires.
Rank elements by measured effect and, where useful, combine the strongest into a fully optimized cassette for a confirmatory round.
Validate the top candidates in your model system, so what you take forward is proven, not predicted.
Find the regulatory sequences that give your gene the right expression behavior.
SuRE™ screens promoters, enhancers and regulatory variants at scale to identify what delivers the right strength, specificity and control.
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