This protocol closes a significant gap between natural evolution of viruses and their use as recombinant vectors for human gene therapy by enabling their engineering, diversification, and stratification. This technique allows for the high-throughput parallel screening for novel, isolated, or engineered adeno-associated virus, in short AAV, variants, thereby saving on animal numbers, work, cost, and time. The identified AVV capsid variants can increase efficacy and specificity of AAV-based delivery of therapeutic transgenes, thereby reducing the required viral dose.
This could improve safety and applicability of gene therapy. The same approach could also be used for capsid or promoter engineering of other gene delivery vehicles which improves our understanding of their biology and their use in gene therapy. Helping to demonstrate the procedure will be PhD students Jonas Becker and Jixin Liu, post-doc Joanna Szumska and Margarita Zayas, as well as research assistants Emma Gerstmann and Ellen Widtke from my laboratory.
Begin by analyzing the NGS sequencing data with Python 3 and Biopython. The NGS analysis is composed of two steps. For the first step, search the sequence files for sequences that satisfy specific criteria such as flanking sequences, length, and location using script one, and a configuration file that provides the information needed.
For the second step, translate the extracted sequences starting at the AGWGGC sequence using script number two and the configuration and zuordnung. txt files that provides the information needed. Prepare two folders:script and data.
Copy the Gzip compressed files resulting from the sequencing to the data folder. Then copy the Python and configuration files to the script folder as described in the text manuscript. Before running the scripts, open the zuordnung.
txt file and add two tab separated columns. Enter the names of the Gzip files in column one and the desired final name in column two. Change the variables in the configuration file as described in the text manuscript.
Using the specific command, initiate the variant sequence detection and extraction. The output will be TXT files with the extracted DNA sequences and their numbers of reads. The header of this file contains statistical data and these data will be transferred to the following files.
These TXT data will be the input file for script number two in which the DNA sequences are translated, ranked, and analyzed. Using the specific command, initiate PV translation and analysis of the text output files of script one as described in the text manuscript. The output files of script number two will be named using the second column of the lookup table in zuordnung.
txt with extensions based on the type of analysis. Ensure that the three output files contain statistical data in the first rows and a first column with the index of each DNA sequence from the input text files. The remaining columns should consist of the DNA sequence, number of reads, forward or reverse read, and translated peptide sequence.
The invalid sequences should have NA and not valid in the last two columns. Visualize the output files using available software based on the user's needs. To perform the analysis of the NGS sequencing data using custom code in Python 3, the workflow comprises the detection of barcode sequences guided by flanking sequences, their length and location, as well as analysis of barcode enrichment and distribution over the set of tissues.
Prepare two folders:script and data. Copy the Gzip compressed files resulting from the sequencing to the data folder. Then copy the python and configuration files to the script folder as described in the text manuscript.
Before executing the script, create two tab delimited text files:the capsidvariance. txt file with the barcode sequences assigned to AAV capsid variant names, and the contamination. txt file with barcode sequences that come from possible contamination.
Finally, edit the configuration file to include the information of the path to folder with sequencing data, sequence of flanking regions of the barcodes, their position, and window size for barcode detection. Execute the barcode detection script with the provided paths and configuration files using the respective command. The output of this command execution will be TXT files with recounts per capsid variant and the total number of reads recovered from the raw data.
To evaluate the distribution of barcoded AAV capsid among tissues or organs, in the zuordnung. txt file, assign the name of each text file obtained from the barcode detection run to a tissue or organ name. Add the names of text files in the first column and corresponding tissue or organ names in the tab delimited assignment.
Create an organs. txt file with the list of names for on and off-target organs, which correspond to the names given in the assignment zuordnung. txt file.
Then create normalization_organ. txt and normalization_variant. txt tab delimited text files with normalized values for all capsid variants and all organs and tissues.
In the first column with the normalization_organ. txt file, write the names given for each organ, and the second column with normalization values for the corresponding tissue. Fill the first column of the normalization_variant.
txt file with the list of capsid names, and the second column with the normalized values of the read counts for each capsid in the pooled library. Edit the configuration file by specifying the full paths to all additional files and execute the barcode analysis script using this specific command. The barcode analysis script outputs several files such as the text files with relative concentration or RC values of capsid distribution within different tissues based on multiple normalization steps described earlier, and the spreadsheet file which combines text file data into merged matrix data.
Visualize the data and perform cluster analysis of the matrix data as described in the text manuscript. The script will input the relativeconcentration. xls files and generate two plots of hierarchical cluster heat map and principal component analysis.
To modify plots or PNG parameters, open the R script and follow the instructions in the comment section. The quantified regions of the AAV2 rep gene showed that 99.2%were positive for ITR, suggesting that the AAV capsids contained the entire viral genomes. In all three sets, the extracted reads based on signature sequences specific to the library represented about 94%of the total reads, indicating good quality.
Of these, more than 99%were valid PV reads and over 99%of the valid PV reads were unique, suggesting that the library was balanced with high internal diversity. The two major branches of the heat map hierarchy reflected the difference in the transduction efficiency of capsid variants. The left branch with the majority of the capsid variants included all the capsids which showed a high relative concentration value across most tissues.
Apart from strikingly high liver specificity, three other capsids exhibited specificity in the diaphragm, skeletal muscle, biceps, and brain. The right branch of the hierarchical clustering included the capsid variants with overall lower transduction efficiency most evident in the duodenum and pancreas. The principal component analysis for the original subset forms the cluster of capsid variants with high liver specificity and outlines the VAR60 capsid outstanding muscle tropism.
When attempting this protocol, you need to pay attention to typing errors. The syntax is also different if you're using the Windows command prompt compared to Linux. Following the identification of novel AAV variants, the therapeutic and translational potential needs to be verified in a deceased or larger animal model.
This technique enables a direct side-by-side comparison of AAV variants under identical conditions and is extremely versatile, paving the way for its translation in large animals, or even in humans.
