Eplet Analysis

Introduction

Eplets are short amino acid sequences that form antibody-binding epitopes on HLA molecules. They are key determinants of HLA immunogenicity and play a crucial role in: - Predicting antibody development risk in transplantation - Understanding patterns of HLA sensitization - Guiding donor selection strategies

This article covers eplet-based analysis in deepMatchR.

Loading the Package

library(deepMatchR)
library(dplyr)

Understanding Eplets

What are Eplets?

Eplets are small configurations of polymorphic amino acid residues on the molecular surface of HLA molecules. They were originally defined by the HLAMatchmaker algorithm and are catalogued in the Eplet Registry.

Key concepts:

Evidence Level: Indicates how well-characterized the eplet is
- A1/A2: Antibody-verified eplets
- B: Theoretically predicted
- D: Deprecated
Exposition: Surface accessibility
- High: Clearly exposed on molecular surface
- Intermediate/Low: Less accessible
Reactivity: Whether antibody binding has been confirmed

Quantifying Eplet Mismatches

Basic Comparison

The quantifyEpletMismatch() function calculates non-shared eplets between two alleles:

# Basic eplet comparison (A1/A2 evidence level by default)
quantifyEpletMismatch("A*01:01", "A*02:01")
#> [1] 13

Filtering by Evidence Level

# Only antibody-verified eplets (A1)
quantifyEpletMismatch("A*01:01", "A*02:01", evidence_level = "A1")
#> [1] 8

# All evidence levels including theoretical
quantifyEpletMismatch("A*01:01", "A*02:01", evidence_level = NULL)
#> [1] 59

Filtering by Exposition

# High-exposition eplets only
quantifyEpletMismatch("B*07:02", "B*44:02", exposition_filter = "High")
#> [1] 6

Filtering by Reactivity

# Confirmed reactive eplets only
quantifyEpletMismatch("C*07:01", "C*06:02",
                      evidence_level = NULL,
                      reactivity_filter = "Confirmed")
#> [1] 9

Calculating Eplet Load

For donor-recipient matching, use calculateEpletLoad() to compute total donor-specific eplets across all loci.

Creating Genotypes

recipient <- data.frame(
  A_1 = "A*01:01", A_2 = "A*02:01",
  B_1 = "B*07:02", B_2 = "B*08:01",
  DQB1_1 = "DQB1*02:02", DQB1_2 = "DQB1*03:01"
)

donor <- data.frame(
  A_1 = "A*03:01", A_2 = "A*24:02",
  B_1 = "B*44:02", B_2 = "B*51:01",
  DQB1_1 = "DQB1*06:02", DQB1_2 = "DQB1*03:01"
)

rgeno <- hlaGeno(recipient)
dgeno <- hlaGeno(donor)

Total Eplet Load

calculateEpletLoad(rgeno, dgeno)
#> [1] 18

Per-Locus Summary

per_locus <- calculateEpletLoad(rgeno, dgeno, return = "per_locus")
per_locus
#>   locus eplet_load
#> 1     A          4
#> 2     B          6
#> 3  DQB1          8

Pairwise Allele Matrix

mB <- calculateEpletLoad(
  rgeno, dgeno,
  return = "pairwise",
  pairwise_locus = "B"
)
mB
#>          donor
#> recipient B*44:02 B*51:01
#>   B*07:02       5       4
#>   B*08:01       4       3

Applying Filters

# High-exposition eplets only
calculateEpletLoad(rgeno, dgeno, exposition_filter = "High")
#> [1] 14

# Antibody-confirmed eplets only
calculateEpletLoad(rgeno, dgeno, reactivity_filter = "Confirmed")
#> [1] 18

# All evidence levels (no filter)
calculateEpletLoad(rgeno, dgeno, evidence_level = NULL)
#> [1] 77

Clinical Interpretation

Eplet Load and DSA Risk

Higher eplet mismatch loads are generally associated with increased risk of donor-specific antibody (DSA) development. However, interpretation should consider:

Evidence quality: A1/A2 eplets have stronger clinical validation
Surface exposition: High-exposition eplets are more accessible to antibodies
Previous sensitization: Patient’s existing antibody profile
Immunosuppression: Treatment protocol intensity

Recommended Thresholds

While specific thresholds vary by center and organ type, general guidelines suggest:

Eplet Load	Risk Level
0-10	Low
11-20	Moderate
>20	High

Note: These are general guidelines. Always consult your institution’s protocols.

Function Reference

`quantifyEpletMismatch()`

Argument	Default	Description
`allele1`, `allele2`	–	Two HLA alleles to compare
`evidence_level`	`c("A1","A2")`	Filter by evidence level
`exposition_filter`	`NULL`	Filter by surface exposition
`reactivity_filter`	`NULL`	Filter by reactivity confirmation

`calculateEpletLoad()`

Argument	Default	Description
`recipient_geno`, `donor_geno`	–	HLA genotypes from `hlaGeno()`
`loci`	`NULL`	Restrict to specific loci
`evidence_level`	`c("A1","A2")`	Filter by evidence level
`exposition_filter`	`NULL`	Filter by surface exposition
`reactivity_filter`	`NULL`	Filter by reactivity confirmation
`return`	`"total"`	`"total"`, `"per_locus"`, or `"pairwise"`
`pairwise_locus`	`NULL`	Locus for pairwise matrix

Session Information

sessionInfo()
#> R version 4.5.2 (2025-10-31)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.3 LTS
#> 
#> Matrix products: default
#> BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
#> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so;  LAPACK version 3.12.0
#> 
#> locale:
#>  [1] LC_CTYPE=C.UTF-8       LC_NUMERIC=C           LC_TIME=C.UTF-8       
#>  [4] LC_COLLATE=C.UTF-8     LC_MONETARY=C.UTF-8    LC_MESSAGES=C.UTF-8   
#>  [7] LC_PAPER=C.UTF-8       LC_NAME=C              LC_ADDRESS=C          
#> [10] LC_TELEPHONE=C         LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C   
#> 
#> time zone: UTC
#> tzcode source: system (glibc)
#> 
#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> other attached packages:
#> [1] dplyr_1.1.4       deepMatchR_0.99.0 BiocStyle_2.38.0 
#> 
#> loaded via a namespace (and not attached):
#>  [1] sass_0.4.10         generics_0.1.4      xml2_1.5.2         
#>  [4] lattice_0.22-7      immReferent_0.99.6  digest_0.6.39      
#>  [7] magrittr_2.0.4      evaluate_1.0.5      grid_4.5.2         
#> [10] RColorBrewer_1.1-3  bookdown_0.46       fastmap_1.2.0      
#> [13] cellranger_1.1.0    jsonlite_2.0.0      Matrix_1.7-4       
#> [16] BiocManager_1.30.27 rvest_1.0.5         httr_1.4.7         
#> [19] scales_1.4.0        Biostrings_2.78.0   textshaping_1.0.4  
#> [22] jquerylib_0.1.4     cli_3.6.5           rlang_1.1.7        
#> [25] crayon_1.5.3        XVector_0.50.0      cachem_1.1.0       
#> [28] yaml_2.3.12         tools_4.5.2         dir.expiry_1.18.0  
#> [31] parallel_4.5.2      memoise_2.0.1       ggplot2_4.0.1      
#> [34] filelock_1.0.3      basilisk_1.22.0     BiocGenerics_0.56.0
#> [37] reticulate_1.44.1   vctrs_0.7.1         R6_2.6.1           
#> [40] png_0.1-8           stats4_4.5.2        lifecycle_1.0.5    
#> [43] pwalign_1.6.0       Seqinfo_1.0.0       IRanges_2.44.0     
#> [46] S4Vectors_0.48.0    fs_1.6.6            ragg_1.5.0         
#> [49] pkgconfig_2.0.3     desc_1.4.3          pkgdown_2.2.0      
#> [52] pillar_1.11.1       bslib_0.10.0        gtable_0.3.6       
#> [55] data.table_1.18.0   glue_1.8.0          Rcpp_1.1.1         
#> [58] systemfonts_1.3.1   xfun_0.56           tibble_3.3.1       
#> [61] tidyselect_1.2.1    knitr_1.51          farver_2.1.2       
#> [64] patchwork_1.3.2     htmltools_0.5.9     rmarkdown_2.30     
#> [67] compiler_4.5.2      S7_0.2.1            readxl_1.4.5

Nick Borcherding

Compiled: January 27, 2026