Genetics of single-cell protein abundance variation in large yeast populations

Abstract

Variation among individuals arises in part from differences in DNA sequences, but the genetic basis for variation in most traits, including common diseases, remains only partly understood. Many DNA variants influence phenotypes by altering the expression level of one or multiple genes. The effects of such variants can be detected as expression quantitative trait loci (eQTL) 1. Traditional eQTL mapping requires large-scale genotype and gene expression data for each individual in the study sample, which limits sample sizes to hundreds of individuals in both humans and model organisms and reduces statistical power 2–6. Consequently, many eQTL are likely missed, especially those with smaller effects 7. Further, most studies use mRNA rather than protein abundance as the measure of gene expression. Studies that have used mass-spectrometry proteomics 8–13 reported surprising differences between eQTL and protein QTL (pQTL) for the same genes 9,10, but these studies have been even more limited in scope. Here, we introduce a powerful method for identifying genetic loci that influence protein expression in the yeast Saccharomyes cerevisiae. We measure single-cell protein abundance through the use of green-fluorescent-protein tags in very large populations of genetically variable cells, and use pooled sequencing to compare allele frequencies across the genome in thousands of individuals with high vs. low protein abundance. We applied this method to 160 genes and detected many more loci per gene than previous studies. We also observed closer correspondence between loci that influence protein abundance and loci that influence mRNA abundance of a given gene. Most loci cluster at hotspot locations that influence multiple proteins—in some cases, more than half of those examined. The variants that underlie these hotspots have profound effects on the gene regulatory network and provide insights into genetic variation in cell physiology between yeast strains.

Genetic variability in the regulation of gene expression in ten regions of the human brain

“…If the latter proportion is large and the power of current studies is low, then two studies of equal power can both discover largely non-overlapping sets of true eQTL signals. This scenario is supported by the recent demonstration of large numbers of protein QTLs of small effect in yeast 28 .…”

Multilayered Genetic and Omics Dissection of Mitochondrial Activity in a Mouse Reference Population

“…Traditionally this was attempted by semiquantitative immunoassays such as ELISA or western blotting, yet these techniques allow only a handful of proteins to be measured in parallel and are moreover limited by the scarcity of quantitative immunoassays and the dubious quality of many antibodies (Marx, 2013). Recent shotgun proteomics experiments on diverse populations have provided fundamental data on protein variance in populations of yeast (Albert et al, 2014; Skelly et al, 2013), mammals (Ghazalpour et al, 2011), and humans (Hwang et al, 2010). However, the inability to measure target proteins has limited the application of this approach in the study of disease pathways.…”

“…Since the advent of microarray technology, comprehensive gene expression patterns—i.e., the transcriptome— can be precisely and comprehensively quantified across large populations. Unfortunately, transcript levels generally have only modest correlation with the levels of corresponding proteins (Ghazalpour et al, 2011; Gygi et al, 1999; Schwan-häusser et al, 2011), and genetic variants similarly affecting both the transcript and peptide levels of a gene are relatively uncommon (Albert et al, 2014; Skelly et al, 2013). As proteins in most cases are more directly responsible than transcripts in the regulation of cellular pathways—and ultimately phenotypic traits—there is a critical need for efficient, large-scale, and accurately quantitative proteomics methods to complement transcriptomic data sets.…”

“…Thus, hundreds of target peptides can be consistently and accurately quantified across large populations of samples. Recent studies in yeast have shown that the proteins and transcripts of genes are typically controlled by different, distinct mechanisms (Albert et al, 2014; Picotti et al, 2013). However, these hypotheses have not been well tested in mammalian genetic reference populations (GRPs) through multilayered transcriptomic and proteomic strategies.…”

Attempt at a systemic outlook on aging and carcinogenesis

“…This view is changing, however; as our knowledge of the function of the genome grows, we obtain mounting evidence that susceptibility to mutations varies from gene to gene. The genome comprises areas that are relatively free of mutations as well as areas in which mutations occur with high frequency [89][90][91] -these are usually referred to as "hotspots". Local susceptibility to mutations may be related to functional hyperactivity or to pathogenic processes such as inflammation, viral infection, or the presence of toxic compounds.…”