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

Albert FW, Treusch S, Shockley AH, Bloom JS, Kruglyak L - Nature (2014)

Bottom Line: The effects of such variants can be detected as expression quantitative trait loci (eQTL).Consequently, many eQTL are probably missed, especially those with smaller effects.We also observed closer correspondence between loci that influence protein abundance and loci that influence mRNA abundance of a given gene.

View Article: PubMed Central - PubMed

Affiliation: 1] Department of Human Genetics, University of California, Los Angeles, California 90095, USA [2] Lewis Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey 08544, USA.

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 several genes. The effects of such variants can be detected as expression quantitative trait loci (eQTL). 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. Consequently, many eQTL are probably missed, especially those with smaller effects. Furthermore, most studies use messenger RNA rather than protein abundance as the measure of gene expression. Studies that have used mass-spectrometry proteomics reported unexpected differences between eQTL and protein QTL (pQTL) for the same genes, 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 Saccharomyces 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 versus 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 that we detected were clustered in 'hotspots' that influence multiple proteins, and some hotspots were found to influence more than half of the proteins that we 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.

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Hotspot effectsA. Distribution of hotspot effects. Red (blue): higher (lower) expression associated with the BY allele. Darker dots: significant X-pQTL. Boxplots show the median (central line), central quartiles (boxes), and data extremes (whiskers).B & C. Effects of the HAP1 and HAP4 hotspots sorted by effect size. Green triangles: direct transcriptional targets of HAP1 or HAP4. Filled triangles: significant X-pQTL.D. Correlation of hotspot effects with expression changes triggered by glucose response. Red circles: genes significantly regulated by the hotspot.E. Effects of the chromosome II hotspot at position 132,948. Green triangles: genes with ribosomal and translation-related functions (Supplementary Table 3).
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Figure 3: Hotspot effectsA. Distribution of hotspot effects. Red (blue): higher (lower) expression associated with the BY allele. Darker dots: significant X-pQTL. Boxplots show the median (central line), central quartiles (boxes), and data extremes (whiskers).B & C. Effects of the HAP1 and HAP4 hotspots sorted by effect size. Green triangles: direct transcriptional targets of HAP1 or HAP4. Filled triangles: significant X-pQTL.D. Correlation of hotspot effects with expression changes triggered by glucose response. Red circles: genes significantly regulated by the hotspot.E. Effects of the chromosome II hotspot at position 132,948. Green triangles: genes with ribosomal and translation-related functions (Supplementary Table 3).

Mentions: The X-pQTL hotspots had widespread effects on protein levels. The median fraction of genes a hotspot affected was 27% of the 160 genes tested, and two of the hotspots each affected more than half of the genes (Extended Data Table 2). The magnitude and direction of expression changes differed considerably among the genes influenced by a given hotspot (Figure 3A). Together, these observations are best explained by hotspots shaping the proteome in a hierarchical manner. Proteins with the largest abundance changes are likely to be closely related in biological function to the gene whose alleles underlie a hotspot. Abundance of more distantly connected proteins may be more weakly perturbed through mechanisms that influence the overall physiological state of the cell 19. The consequences of some genetic differences may thus reverberate through much of the cell. We illustrate these ideas with a closer look at three of the hotspots.


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

Albert FW, Treusch S, Shockley AH, Bloom JS, Kruglyak L - Nature (2014)

Hotspot effectsA. Distribution of hotspot effects. Red (blue): higher (lower) expression associated with the BY allele. Darker dots: significant X-pQTL. Boxplots show the median (central line), central quartiles (boxes), and data extremes (whiskers).B & C. Effects of the HAP1 and HAP4 hotspots sorted by effect size. Green triangles: direct transcriptional targets of HAP1 or HAP4. Filled triangles: significant X-pQTL.D. Correlation of hotspot effects with expression changes triggered by glucose response. Red circles: genes significantly regulated by the hotspot.E. Effects of the chromosome II hotspot at position 132,948. Green triangles: genes with ribosomal and translation-related functions (Supplementary Table 3).
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4285441&req=5

Figure 3: Hotspot effectsA. Distribution of hotspot effects. Red (blue): higher (lower) expression associated with the BY allele. Darker dots: significant X-pQTL. Boxplots show the median (central line), central quartiles (boxes), and data extremes (whiskers).B & C. Effects of the HAP1 and HAP4 hotspots sorted by effect size. Green triangles: direct transcriptional targets of HAP1 or HAP4. Filled triangles: significant X-pQTL.D. Correlation of hotspot effects with expression changes triggered by glucose response. Red circles: genes significantly regulated by the hotspot.E. Effects of the chromosome II hotspot at position 132,948. Green triangles: genes with ribosomal and translation-related functions (Supplementary Table 3).
Mentions: The X-pQTL hotspots had widespread effects on protein levels. The median fraction of genes a hotspot affected was 27% of the 160 genes tested, and two of the hotspots each affected more than half of the genes (Extended Data Table 2). The magnitude and direction of expression changes differed considerably among the genes influenced by a given hotspot (Figure 3A). Together, these observations are best explained by hotspots shaping the proteome in a hierarchical manner. Proteins with the largest abundance changes are likely to be closely related in biological function to the gene whose alleles underlie a hotspot. Abundance of more distantly connected proteins may be more weakly perturbed through mechanisms that influence the overall physiological state of the cell 19. The consequences of some genetic differences may thus reverberate through much of the cell. We illustrate these ideas with a closer look at three of the hotspots.

Bottom Line: The effects of such variants can be detected as expression quantitative trait loci (eQTL).Consequently, many eQTL are probably missed, especially those with smaller effects.We also observed closer correspondence between loci that influence protein abundance and loci that influence mRNA abundance of a given gene.

View Article: PubMed Central - PubMed

Affiliation: 1] Department of Human Genetics, University of California, Los Angeles, California 90095, USA [2] Lewis Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey 08544, USA.

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 several genes. The effects of such variants can be detected as expression quantitative trait loci (eQTL). 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. Consequently, many eQTL are probably missed, especially those with smaller effects. Furthermore, most studies use messenger RNA rather than protein abundance as the measure of gene expression. Studies that have used mass-spectrometry proteomics reported unexpected differences between eQTL and protein QTL (pQTL) for the same genes, 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 Saccharomyces 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 versus 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 that we detected were clustered in 'hotspots' that influence multiple proteins, and some hotspots were found to influence more than half of the proteins that we 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.

Show MeSH
Related in: MedlinePlus