In silico Derivation of HLA-Specific Alloreactivity Potential from Whole Exome Sequencing of Stem-Cell Transplant Donors and Recipients: Understanding the Quantitative Immunobiology of Allogeneic Transplantation

Abstract

Donor T-cell mediated graft versus host (GVH) effects may result from the aggregate alloreactivity to minor histocompatibility antigens (mHA) presented by the human leukocyte antigen (HLA) molecules in each donor-recipient pair undergoing stem-cell transplantation (SCT). Whole exome sequencing has previously demonstrated a large number of non-synonymous single nucleotide polymorphisms (SNP) present in HLA-matched recipients of SCT donors (GVH direction). The nucleotide sequence flanking each of these SNPs was obtained and the amino acid sequence determined. All the possible nonameric peptides incorporating the variant amino acid resulting from these SNPs were interrogated in silico for their likelihood to be presented by the HLA class I molecules using the Immune Epitope Database stabilized matrix method (SMM) and NetMHCpan algorithms. The SMM algorithm predicted that a median of 18,396 peptides weakly bound HLA class I molecules in individual SCT recipients, and 2,254 peptides displayed strong binding. A similar library of presented peptides was identified when the data were interrogated using the NetMHCpan algorithm. The bioinformatic algorithm presented here demonstrates that there may be a high level of mHA variation in HLA-matched individuals, constituting a HLA-specific alloreactivity potential.

Keywords: HLA; alloreactivity potential; minor histocompatibility antigen; stem-cell transplant; whole exome sequencing.

Figure 1

Bioinformatics workflow for calculating HLA-specific alloreactivity potential in individual DRP. Starting with donor and recipient whole exome sequence data, non-synonymous SNP with a GVH vector (nsSNP_GVH) were identified, and peptide fragments generated using the ANNOVAR software package. These peptides, together with HLA data (Table 1) were then analyzed with IEDB SMM and NetMHCpan algorithms separately. Individual DRP binding data were then analyzed and candidate mHAs cataloged.

Figure 2

The burden of minor histoincompatibility in human SCT. (A) All possible mHA in human beings: data generated from NCBI dbSNP database (22). (B) Alloreactivity potential: the current patient cohort had an average of 6,445 nsSNPs/DRP, which when converted into peptide fragments averaged 486,463 possible mHA/DRP. (C) Putative mHA: each DRP had its nsSNP_GVH-encoded peptides filtered by predicted binding to six HLA class I alleles specific to that DRP. Average number of peptides with binding affinity labeled presented (<500 nM), and strongly presented (<50 nM) is shown.

Figure 3

Whole exome sequence variation and resulting HLA-binding oligopeptides in MRD and URD. (A) Number of nsSNP, and the resulting presented (IC50 < 500 nM) and strongly presented (IC50 < 50 nM) peptides (GVH vector) presented by the HLA in each patient. (B) Same data as in (A), presented with the y-axis changed to log-scale to better illustrate the SNP to HLA-binding peptide ratio between MRD and URD. Significant difference observed in the distribution of SMM-IEDB predicted presented and strongly presented peptides between MRD and URD. Patients 2, 4, 16, 23 – MRD; patients 3, 5, 7, 8, 10 – URD SCT recipients.

Figure 4

Peptide-HLA complexes with IC50 values up to 100 nM plotted in descending order of binding affinity. Depicting difference in the number of peptide-HLA complexes (x-axis) and their IC50 values (y-axis), for each DRP. Lower IC50 values correspond to greater binding affinity between putative peptide and relevant HLA. IC50 distribution is non-linear and described as a polynomial function of the second order, forming a continuum. Marked difference observed between MRD and URD (see Table 2) for the AUC calculated from these plots.

Figure 5

Unique peptide-HLA complexes (GVH vector) with IC50 < 500 nM predicted by both SMM and NetMHCpan. Scatter plots depict the IC50 for unique polymorphic peptide-HLA complexes predicted by the two different algorithms studied. Each circle corresponds to a unique peptide-HLA complex, with color depicting specific HLA. A large number of patient-HLA-specific strong-binding peptides identified by both programs, using SNP data derived from exome sequencing. Only shared peptide-HLA complexes predicted to have an IC50 < 500 nM by both algorithms included.

Figure 6

Tissue distribution of presented mHA with gene expression. Number of genes coding for mHA (IC50 < 500 nM by SMM algorithm) and expressed at a relative expression unit (REU) value > 10. European Bioinformatics Institute Illumina Body Map was used to correlate presented peptides with relative gene expression in 16 tissues. Several hundred genes per organ expressed have nsSNP_GVH, which may generate HLA binding peptides (SMM IEDB dataset).

Figure 7

A quantitative model for the development of GVHD. Whole exome sequencing identifies all the nsSNP with a GVH vector, yielding a putative alloreactivity potential, which may be a function (f) of the cumulative influence of these polymorphisms. This is represented as a series, listing the sequence of polymorphic exome loci. Substituting individual nsSNP_GVH in the equation by peptide-HLA binding affinity (reciprocal of IC50)*relative expression level of the gene bearing the nsSNP_GVH (for each HLA molecule) yields the HLA-specific alloreactivity potential, in this Re is the relative expression of protein with nsSNP_GVH and resulting peptides. In this series, the expression, Re_p1*(1/IC50_P1-HLA-A1) for each specific peptide-HLA complex, hypothetically represents the T-cell clone-specific AP. Multiple peptides constituting this series then drive a proportional oligoclonal T-cell expansion in GVHD, as many different mHA are presented by the HLA in an individual, the final distribution conforming to the Power law. Since T-cell clonal expansion in response to presented antigens may be influenced by factors such as tissue injury, cytokine milieu, and immunosuppression intensity; the GVHD likelihood, and its phenotype may in turn be determined not only by the ubiquitous mHA but also by the tissue volume and its state (inflammation/injury), and most importantly time at which organ injury/inflammation occurs relative to T-cell infusion.