Nucleic Acid Regulatory Elements for Compact Tissue-Specific Expression
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Solution Overview
Problem
Existing methods for engineering nucleic acid regulatory elements (NAREs) are time-consuming and expensive, requiring optimization for enhanced potency, reduced size, and tissue specificity, which are crucial for efficient gene therapy applications.
Innovation Solution
Development of nucleic acid regulatory elements (NAREs) with specific sequences that are at least 80-95% identical to reference sequences, optimized for constitutive expression in tissues like liver, muscle, and the CNS, allowing for efficient packaging and targeted gene expression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional low-throughput rational design or high-throughput MPRA methodologies are used to engineer NAREs, then NARE sequences can be optimized for potency and tissue specificity, but the process becomes expensive and time-consuming
Solution Approach 1:
The patent uses deep learning models trained on existing NARE sequences to predict and generate optimized NARE sequences without requiring extensive in vitro testing. The model learns from training data containing known functional NARE sequences and their expression patterns, then generates new sequences with desired properties directly in silico, dramatically reducing the time and cost of NARE engineering while maintaining high potency and tissue specificity
Solution Approach 2:
The patent employs deep learning models that can predict NARE function based on sequence parameters and generate sequences with optimized parameters for specific tissues. By changing sequence parameters through computational prediction rather than iterative experimental optimization, the patent achieves rapid engineering of NAREs with enhanced potency and tissue-specific expression patterns
2Volume of moving object
If NARE size is reduced to allow efficient packaging into viral vectors, then larger transgene cargo can be packaged, but NARE strength and specificity may be compromised
Solution Approach 1:
The deep learning model is trained to predict NARE function from sequence data, allowing the identification of minimal sequences that maintain full functional activity. By copying successful sequence patterns from training data and generating optimized short sequences, the patent produces compact NAREs that fit within viral vector packaging limits while preserving or enhancing their expression strength and tissue specificity
Solution Approach 2:
The patent extracts only the essential functional elements required for NARE activity by using the deep learning model to identify and generate minimal sequences. The model learns to distinguish critical functional regions from non-essential sequences, enabling the creation of truncated or minimized NAREs that retain full potency and tissue specificity while reducing overall size for efficient viral vector packaging
3Reliability
If larger amounts of gene therapy vector are administered to achieve desired expression levels, then sufficient transgene expression can be achieved, but immune responses and safety risks increase
Solution Approach 1:
The deep learning model generates NARE sequences with optimized parameters for high expression potency, allowing achievement of therapeutic expression levels with lower vector doses. By changing NARE sequence parameters to maximize transcriptional activity and tissue-specific expression, the patent enables sufficient transgene expression with reduced vector administration, thereby minimizing immune responses and safety risks
Data Source
AI summary
The application relates to nucleic acid regulatory elements that are able to enhance expression of genes constitutively in a variety of tissues, or in particular tissues including liver, muscle, and the CNS. The application further relates to methods employing these regulatory elements and uses of these elements. Expression cassettes and vectors containing these nucleic acid regulatory elements are also disclosed. These nucleic acid regulatory elements are particularly useful for applications using gene therapy.