wwang-nmdp / ViTA

Predict genomic variants encoded HLA-epitopes

ViTA

Targeted genome editing has enormous potentia for treating monogenic disorders, especially for those that affect the hematopoietic system. Reducing the immunogenicity of gene-editing components such as CRISPR-Cas9 is critical for ensuring safe treatment.

Gene corrections may also result in novel epitopes of human leukocyte antigen (HLA).

ViTA, Variants To Antigen, is a java application that predicts immunogenicity of HLA-epitopes. It supports common input formats: Variant call format (VCF) and Single-variant.

Background

CRISPR/Cas9 and other gene-editing technologies have exciting therapeutic potential. Clinical applications include curing monogenetic disorders or humanizing animal organs for xenotransplantation[1]. Considerable effort and progress has been made to reduce the immunogenicity of microbial-derived core editing components and delivery vectors[2].

However, relatively little attention is given to the possibility that somatic gene edits may lead to immunoreactive antigens introduced ex-post facto (Figure: ViTA Algorithmic Flow Chart).

The effects could be counterproductive if immunocompetent patients reject the therapy or life threatening in cases where human leukocyte antigen (HLA)-bound peptides elicit an alloreactive-like response[3].

Here, we present a strategy paired with an efficient application, variant-to-antigen (ViTA), to simulate gene-edited peptide presentation.

Algorithmic Flow Chart

Pre-installation

####Install SnpEff[4] from http://snpeff.sourceforge.net/index.html SnpEff is a variant annotation and effect prediction tool. It annotates and predicts the effects of variants on genes (such as amino acid changes).

wget http://sourceforge.net/projects/snpeff/files/snpEff_latest_core.zip
unzip snpEff_latest_core.zip
java -jar snpEff.jar

Run 'java -jar snpEff.jar command' for help on each specific command

Usage: snpEff build [options] genome_version java -jar snpEff.jar build GRCh38.82 Install netChop3.1[5] from Center For Biological Sequence Analysis, Technical University of Denmark. The NetChop 3.1 may be downloaded only by special agreement. For academic users there is a download site at:http://www.cbs.dtu.dk/cgi-bin/nph-sw_request?netchop. Other users are requested to contact [e-mail].

cat netchop-3.1.<unix>.tar.Z | uncompress | tar xvf -
export NETCHOP=/full/path/to/netchop-3.1
export TMPDIR=/full/path/to/netchop-3.1/tmp

In the 'netchop-3.1' directory test the software: bin/netchop test/test.fsa > test.out For research purpose, the net Chop3.1 is pre-built in MiHA identification pipeline. Make sure two environment variables, NETCHOPandTMDIR are set properly.Alsomakesurethat TMPDIR has the sticky bit set (the long listing should render 'drwxrwxrwt...'). If not, set it:

chmod 1777 $TMPDIR

Install netMHCpan-3.0[6] from Center For Biological Sequence Analysis, Technical University of Denmark.

Like netChop, the netMHCpan-3.0 may only be downloaded only by special agreement as well. In addition, it requests to download the data file (data.tar.gz) separately.

cat netMHCpan-3.0.<unix>.tar.gz | uncompress | tar xvf -

cd ./netMHCpan-3.0
wget http://www.cbs.dtu.dk/services/NetMHCpan-3.0/data.tar.gz
tar -xvf data.tar.gz

Set environment variables

export TMPDIR=/Path/to/Tools/netMHCpan-3.0/tmp
export NMHOME=/Path/to/Tools/netMHCpan-3.0/bin
export NETMHCpan=/Path/to/Tools/netMHCpan-3.0

Build database of Transcriptome

cds.db is pre-built hg38 transcriptome and minor allele frequence (version:snp147common) database, please download by using the provided link: https://sourceforge.net/projects/mihaip/files/cds.db.zip/download

unzip the file:

unzip cds.db.zip

Copy the database into your working directory.

Run ViTA

java -jar ViTA-1.0.jar -h
A typical command line would be:   java -jar vita.jar -i /path/to/myVariants.vcf -hla A02:01,B07:02,C04:02 -w -t /path/to/workingDirectory/Tools -o /path/to/output
Commands:
   -i,     Multiple or single variant input file
           input file should be a tab-delimited format, regardless of the file extensions.

   -pos,   Position of variants at chromosome coordinate
           simply input the chromosome coordinate. e.g. -pos chr11:5227002
           Be cautious, the position format have to be 1-based
           Please follow the instruction of UCSC genome browser for details of coordinate system: 
               http://genome.ucsc.edu/blog/the-ucsc-genome-browser-coordinate-counting-systems/
           It can only take either -i or -pos as input for each process

   -hla,   List of HLA alleles you wish to access immunogenicities
           HLA allele should be 4-digit resolution with one upper-case allele type followed by 4 numbers.
               e.g. A01:01
           Multiple allele input are allowed by comma-delimited format
               e.g. A0101,A,2001,A0301,B0702
           Depends on the number of CPUs and disk writing speed, multiple allele input could significantly reduce the predictions
   -t,     Tell the program where are the dependency tools
           Set the path to netChop and netMHCpan
           You might also need to set the PATH for the dependencies:
               export TMPDIR=/path/to/Tools/netMHCpan-3.0/tmp
               export TMPDIR=/path/to/Tools/netchop-3.1/tmp
               export NETCHOP=/path/to/Tools/netchop-3.1/
               export NETMHCpan=/path/to/Tools/netMHCpan-3.0

   -w,    
           To activate the slidingWindow function for chopping out all possible variant-containing 8- to 11- mer
           Otherwise the program goes netChop as default
   -o,     Set a directory to store the output
              e.g. /path/to/test/output
          

Miscellaneous:
   -v,     Print current version information and exit
   -h,     Print this help and exit

#check options of the application and test with sample data

References:

[1] Meier, R.P., et al. Xenotransplantation: back to the future?. Transplant International, 2018.31(5): p.465-477.

[2] Chew WL., et al., Immunity to CRISPR Cas9 and Cas12a therapeutics. Wiley Interdisciplinary Reviews: Systems Biology and Medicine. 2018 Jan;10(1): p. 1408.

[3] Horowitz, M.M., et al., Graft-versus-leukemia reactions after bone marrow transplantation. Blood, 1990. 75(3): p. 555-62.

[4] Cingolani, P., et al., A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly (Austin), 2012. 6(2): p. 80-92.

[5] Kesmir, C., et al., Prediction of proteasome cleavage motifs by neural networks. Protein Eng, 2002. 15(4): p. 287-96.

[6] Nielsen, M. and M. Andreatta, NetMHCpan-3.0; improved prediction of binding to MHC class I molecules integrating information from multiple receptor and peptide length datasets. Genome Med, 2016. 8(1): p. 33.