Barcoded Influenza Viruses With Stable Packaging Signal Insertion
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Solution Overview
Problem
Existing methods for barcoding influenza viruses to study mutations face challenges such as barcode deletion during replication, leading to experimental noise and inaccurate results, and lack efficient, high-throughput methods for assessing viral resistance and neutralization.
Innovation Solution
The method involves duplicating and recoding the 5' vRNA packaging signal within the viral genome segment's open reading frame to reduce sequence identity and inserting a nucleic acid barcode, with additional stop codons to ensure barcode retention, allowing for high-throughput sequencing and accurate analysis of viral protein mutations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a barcode is inserted into the viral genome segment, then high-throughput sequencing and analysis of viral mutations become possible, but barcode deletion occurs during viral replication leading to experimental noise
Solution Approach 1:
The patent divides the viral genome segment into distinct functional regions: the open reading frame (ORF) containing the viral protein coding sequence, and the downstream packaging signal region. The barcode is inserted between these regions, separating it from both the coding sequence and the packaging signal. This segmentation prevents the barcode from being deleted during replication while maintaining its functionality for sequencing analysis.
Solution Approach 2:
The patent introduces a packaging signal sequence as an intermediary element between the barcode and the natural packaging signal. This intermediary packaging signal ensures proper viral genome packaging while protecting the barcode from deletion. The intermediary packaging signal acts as a buffer that maintains barcode integrity during viral replication cycles.
2Reliability
If the barcode is placed within the open reading frame, then viral protein function is maintained, but sequence identity with packaging signal increases causing barcode loss
Solution Approach 1:
The patent extracts the barcode from the open reading frame and places it in the downstream region after the ORF. This extraction maintains the integrity of the viral protein coding sequence while positioning the barcode in a location where it cannot be deleted during replication. The barcode is taken out of the functional coding region and placed in a non-coding region.
Solution Approach 2:
The patent transitions from placing the barcode within the one-dimensional coding sequence to positioning it in the downstream region, adding a dimensional separation between the coding function and the barcode function. This spatial repositioning in the genomic architecture allows the barcode to exist independently of the packaging signal sequence.
3Adaptability or versatility
If standard barcoding methods are used, then viral libraries can be created, but experimental noise from barcode deletion reduces measurement precision
Solution Approach 1:
The patent applies beforehand cushioning by inserting an intermediary packaging signal sequence before the barcode region. This packaging signal acts as a protective cushion that prevents barcode deletion during viral replication. By preparing this protective structure in advance, the barcode remains stable throughout the viral life cycle, ensuring accurate measurement results.
Solution Approach 2:
The patent uses a synthetic DNA construct that copies the essential packaging signal functionality without containing the barcode within the coding region. This synthetic construct serves as a template for generating viral libraries with stable barcodes. The copying of packaging signal function allows proper viral assembly while maintaining barcode integrity for accurate sequencing analysis.
Data Source
AI summary
Methods to create barcoded influenza viruses without disrupting the function of the viral proteins and the proper packaging of the viral genome segments are described. The barcoded influenza viruses can be used within deep mutational scanning libraries to map influenza resistance mutations to therapeutic treatments. The libraries can also be used to predict influenza strains that may become resistant to therapeutic treatments and/or more easily evolve to infect new species.