Defective Interfering Viral Genome Identification via Temporal Frequency Analysis
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Solution Overview
Problem
Current methods for identifying and generating defective interfering viral genomes (DVGs) are limited, often biased towards short or abundant DVGs, which may not be the most effective in competing with wild-type viruses, and lack reproducibility and rationality across different virus families.
Innovation Solution
A combined experimental and computational approach that generates all possible DVGs and predicts the best candidates using temporal frequency analysis, allowing for the identification of thousands of DVGs, including deletion DVGs, which are genetically engineered and tested for their ability to interfere with wild-type virus replication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If classic methods such as PCR amplification are used to isolate DVGs, then only one or two DVGs can be identified, but this approach is biased towards the shortest or most abundant DVGs and does not provide comprehensive identification
Solution Approach 1:
The patent segments the viral genome into multiple regions and uses targeted primers for each region to amplify and sequence DVGs. This segmentation allows comprehensive coverage of the entire genome, identifying numerous DVGs with different deletion patterns rather than being biased towards only the shortest or most abundant ones.
Solution Approach 2:
The patent transitions from traditional single-point detection methods to a multi-dimensional approach using next-generation sequencing that captures temporal frequency data across multiple passages and replicates. This dimensional expansion enables identification of thousands of DVGs with varying frequencies and characteristics.
2Adaptability or versatility
If DVGs are generated through random mutagenesis, then a diverse population of DVGs can be produced, but it is difficult to predict which DVGs will be most effective at interfering with wild-type virus
Solution Approach 1:
The patent implements feedback by sequencing DVGs across multiple passages and replicates, tracking their temporal frequencies and evolutionary trajectories. This feedback loop allows identification of DVGs that consistently emerge and persist, indicating high interference potential, while discarding those that are transient or non-interfering.
Solution Approach 2:
The patent systematically varies key parameters including passage number, replicate number, and sequencing depth to generate comprehensive data on DVG frequencies. By analyzing changes in DVG parameters across these dimensions, the study identifies which DVGs have the highest interference capacity against wild-type virus.
3Quantity of substance
If a comprehensive approach is used to identify all possible DVGs, then thousands of DVGs can be generated and analyzed, but the complexity of the experimental and computational workflow increases
Solution Approach 1:
The complex workflow is segmented into distinct modular components: virus passage and culture protocols, RNA extraction and quality control, next-generation sequencing library preparation, bioinformatic analysis pipelines, and interference assays. Each module can be independently optimized and replicated, reducing overall complexity while enabling comprehensive DVG generation.
Solution Approach 2:
The patent uses computational methods to create virtual copies and models of DVGs based on sequence data, allowing extensive analysis without requiring physical replication of each variant. Bioinformatic tools simulate and predict DVG behavior, reducing the need for exhaustive experimental testing of every possible DVG.
4Ease of manufacture
If only the most abundant DVGs are isolated using traditional methods, then the isolation process is simplified, but these DVGs are not necessarily the best candidates for competing with wild-type virus
Solution Approach 1:
The patent adds temporal and replicational dimensions to DVG analysis, tracking frequencies across multiple passages and biological replicates. This dimensional expansion reveals that DVGs with lower initial abundance but consistent temporal frequency patterns are superior interferers, correcting the traditional bias towards only the most abundant variants.
Solution Approach 2:
The patent uses feedback from interference assays and temporal frequency data to identify and select the most effective DVG candidates. By continuously monitoring DVG performance against wild-type virus and adjusting selection criteria based on this feedback, the study identifies reliable interferers that may not be the most abundant but are the most effective.
Data Source
AI summary
Method for producing a defective interfering viral genome (DVG), defective interfering particles comprising the DVG, and methods and uses thereof.