Can't say I've ever used autoplot, but this is fairly easy to achieve using "base" ggplot.
One thing autoplot does is put the axis on the same scale reflecting the percentage variation captured by each PC - I think this is a good thing, but most PCA plots you'll see don't do this.
As you haven't given us any info on your metadata, I'll just make a few assumptions - lets make it a data.frame called "colData" with a "Status" column.
prcomp stores the PC scores in "pca_res$x"
# generate some metadata
colData <- data.frame(row.names=rownames(pca_res$x),status=as.factor(sample(c("diseased","healthy"),nrow(pca_res$x),replace = T)))
# get % variation for each PC
pca_res$percentVar <- pca_res$sdev^2/sum(pca_res$sdev^2)
# change scale to reflect % variation
d <- t(data.frame(t(pca_res$x)*pca_res$percentVar))
# merge metadata and modified PC scores by row.names (by=0)
d <- merge(d,colData,by=0)
# make the base plot (PC1 vs PC2)
g <- ggplot(d,aes(x=PC1,y=PC2,colour=status))
# add the points
g <- g + geom_point(size=2)
# add a "wow" factor to the points
# display the plot
There may be a way to do this with autoplot - e.g.
gets you part of the way there, but I think using the standard ggplot syntax is easy enough anyway.