Prediction of HY Associated Epitopes

Import libraries

Code 
suppressPackageStartupMessages({
library(tidyverse)
library(seqinr)
library(tibble)
library(epitopeR)
})

# system folder to pull out samle data tables
fd_dat <- "extdata/vgn/data"

# local function for ggplot()
theme_lcl <- function(){
  theme(plot.title = element_text(hjust = 0.5, size = 6),
        axis.title = element_text(size = 4),
        legend.title = element_text(size = 4),
        axis.text.x = element_text(angle = 30, hjust = 0.5, vjust = 0.8, size = 4),  
        axis.text.y = element_text(size = 4),  
        legend.text = element_text(size = 4),
        legend.key.width = unit(0.2, "cm"),
        legend.key.height = unit(0.2, "cm"),
        legend.position = "bottom")
}

pick relevant proteins

MGI gene expression search results
Field input: ChrY, wild-type only, results from all assay types, Anatomic location (one spleen, one skin)
Filter: detected==“yes” Two files, one is genes expressed in Skin, the other is genes expressed in Spleen

Code 
spleen <- read.delim(system.file(fd_dat, "MGIgeneExpr_ChrY_spleen.txt", package = "epitopeR")) %>%
  select(Gene.Symbol, Strain, Sex, Structure)

skin <- read.delim(system.file(fd_dat, "MGIgeneExpr_ChrY_skin.txt", package = "epitopeR")) %>%
  select(Gene.Symbol, Strain, Sex, Structure)
Code 
gex <- spleen %>%
        bind_rows(skin) %>%
        mutate(Gene.Symbol = as.factor(Gene.Symbol)) %>%
        select(-c(Strain, Sex)) %>%
        unique()

gex %>%
    ggplot(aes(Gene.Symbol, fill = Structure)) + 
    geom_bar() +
    ggtitle("HY Gene Expression by Tissue") +
    theme_lcl()

Focus on Utx, DDx3y (or Dby), and Kdm5d(or Smcy)

Code 
# stim, donor, foreign: y
# self: x
# presenting - H2-IAb for class II, "H-2-Db" and "H-2-Db" for class I

utx <- read.fasta(system.file(fd_dat, "utx_r.fasta", package = "epitopeR"), 
                  as.string = TRUE, seqonly = TRUE) %>% unlist

uty <- read.fasta(system.file(fd_dat, "uty_d.fasta", package = "epitopeR"), 
                  as.string = TRUE, seqonly = TRUE) %>% unlist

ddx3x <- read.fasta(system.file(fd_dat, "ddx3x.fasta", package = "epitopeR"), 
                    as.string = TRUE, seqonly = TRUE) %>% unlist

ddx3y <- read.fasta(system.file(fd_dat, "ddx3y.fasta", package = "epitopeR"), 
                    as.string = TRUE, seqonly = TRUE) %>% unlist

kdm5c <- read.fasta(system.file(fd_dat, "kdm5c.fasta", package = "epitopeR"), 
                    as.string = TRUE, seqonly = TRUE) %>% unlist

kdm5d <- read.fasta(system.file(fd_dat, "kdm5d.fasta", package = "epitopeR"), 
                    as.string = TRUE, seqonly = TRUE) %>% unlist

rm(fd_dat)

MHC class I

Code 
mhcI_wrapper <- function(pres_in, stim_in, self_in, len_in, mth_in){
  out <- mhcI(ag_present = pres_in, 
              ag_stim = stim_in, 
              ag_self = self_in,
              seq_len = len_in, method = mth_in)
  return(out)
}

pres1 <- c("H-2-Db", "H-2-Kb")
len1 <- "9"
mth1 <- "recommended"

re_ut <- re_ddx3 <- re_kdm5 <- data.frame()

for (i in 1:length(pres1))
{
  tmp <- mhcI_wrapper(pres_in = pres1[i], stim_in = uty, self_in = utx,
                      len_in = len1, mth_in = mth1)
  re_ut <- re_ut %>% rbind(tmp)
  rm(tmp)
  
  tmp <- mhcI_wrapper(pres_in = pres1[i], stim_in = ddx3y, self_in = ddx3x,
                      len_in = len1, mth_in = mth1)
  re_ddx3 <- re_ddx3 %>% rbind(tmp)
  rm(tmp)

  tmp <- mhcI_wrapper(pres_in = pres1[i], stim_in = kdm5d, self_in = kdm5c,
                      len_in = len1, mth_in = mth1)
  re_kdm5 <- re_kdm5 %>% rbind(tmp)
  rm(tmp)
}

re_mhc1 <- rbind(re_ut, re_ddx3, re_kdm5)

rm(mhcI_wrapper, pres1, len1, mth1, re_ut, re_ddx3, re_kdm5)

MHC class II

Code 
pres2 <- c("H2-IAb")
len2 <- "15"
mth2 <- "recommended"

re_ut <- mhcII(ag_stim = uty, 
               ag_self = utx,
               ag_present = pres2, seq_len = len2, method = mth2,
               nm_stim = "uty",
               nm_self = "utx")

re_ddx3 <- mhcII(ag_stim = ddx3y, 
                 ag_self = ddx3x,
                 ag_present = pres2, seq_len = len2, method = mth2,
                 nm_stim = "ddx3y",
                 nm_self = "ddx3x")

re_kdm5 <- mhcII(ag_stim = kdm5d, 
                 ag_self = kdm5c,
                 ag_present = pres2, seq_len = len2, method = mth2,
                 nm_stim = "kdm5d",
                 nm_self = "kdm5c")

re_mhc2 <- rbind(re_ut, re_ddx3, re_kdm5)

rm(pres2, len2, mth2, re_ut, re_ddx3, re_kdm5)

List of known peptides

Code 
HY_peptides <- c("WMHHNMDLI", "KCSRNRQYL", "NAGFNSNRANSSRSS")

Display predicted peptides with known peptides highlighted, facet by allele and by protein

Code 
out <- re_mhc2 %>%
        select(allele, antigen, pep_stim, score_val, rank_val) %>%
        rbind(re_mhc1 %>% select(allele, antigen, pep_stim, 
                                 score_val = score, rank_val = percentile_rank))  %>%
         #mutate(known = ifelse(pep_stim %in% HY_peptides, "known", "other"))
  mutate(known = ifelse(pep_stim %in% HY_peptides, "yes", "no"))

out %>%
  filter(allele %in% c("H-2-Kb", "H-2-Db")) %>%
   ggplot(aes(rank_val, score_val, color = known)) + 
   geom_jitter(size = 0.5, alpha = 0.7, position = position_jitter(width = .2)) +
  scale_color_manual(values=c("cornflowerblue", "red")) +
   facet_grid(allele~antigen, scales = "free") + 
   ggtitle("Predicted Peptides by Score and Percent Rank") +
   ylab("Elution Likelihood Score") + 
   xlab("Adjusted Percent Rank") +
   scale_size_continuous(guide = "none") + 
   theme_lcl()

Code 

out %>%
   filter(allele %in% c("H2-IAb")) %>%
   ggplot(aes(rank_val, score_val, color = known)) + 
   geom_jitter(size = 0.5, alpha = 0.7, position = position_jitter(width = .2)) +
   scale_color_manual(values=c("cornflowerblue", "red")) +
   facet_grid(allele~antigen, scales = "free") + 
   ylab("IC50") + 
   xlab("Adjusted Percent Rank") +
   ggtitle("Predicted Peptides by Score and Percent Rank") +
   #scale_size_continuous(guide = "none") + 
   theme_lcl()

References:

  1. MGI Mouse Gene Expression Database
  2. Protein sequences Uniprot
  3. Smcy Epitope Scott 1995, Nature. Identification of a mouse male-specific transplantation antigen, H-Y
  4. Utx Epitope Greenfield 1996, Nature Genetics. An H–YDb epitope is encoded by a novel mouse Y chromosome gene
  5. Ddx3y Epitope Lantz 2000, Nature Immunology. γ chain required for naïve CD4+ T cell survival but not for antigen proliferation