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PreprintCited by 11Ubiquitin and proteasome pathwaysAutophagy in Disease and TherapyFungal and yeast genetics research

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Substrate-Specific Effects of Natural Genetic Variation on Proteasome Activity

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Collins, Mahlon A., Avery, Randi R., Albert, Frank W.·2021·PLoS Genetics
DOI 10.1101/2021.11.23.469794PMC PMC10174532

Preprint — published as 10.1371/journal.pgen.1010734 · Paper page

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Abstract

Protein degradation is an essential biological process that regulates protein abundance and removes misfolded and damaged proteins from cells. In eukaryotes, most protein degradation occurs through the stepwise actions of two functionally distinct entities, the ubiquitin system and the proteasome. Ubiquitin system enzymes attach ubiquitin to cellular proteins, targeting them for degradation. The proteasome then selectively binds and degrades ubiquitinated substrate proteins. Genetic variation in ubiquitin system genes creates heritable differences in the degradation of their substrates. However, the challenges of measuring the degradative activity of the proteasome independently of the ubiquitin system in large samples have limited our understanding of genetic influences on the proteasome. Here, using the yeast Saccharomyces cerevisiae , we built and characterized reporters that provide high-throughput, ubiquitin system-independent measurements of proteasome activity. Using single-cell measurements of proteasome activity from millions of genetically diverse yeast cells, we mapped 15 loci across the genome that influence proteasomal protein degradation. Twelve of these 15 loci exerted specific effects on the degradation of two distinct proteasome substrates, revealing a high degree of substrate-specificity in the genetics of proteasome activity. Using CRISPR-Cas9-based allelic engineering, we resolved a locus to a causal variant in the promoter of RPT6 , a gene that encodes a subunit of the proteasome’s 19S regulatory particle. Our results reveal the complex genetic architecture of proteasome activity and suggest that genetic influences on the proteasome may be an important source of variation in the many cellular and organismal traits shaped by protein degradation. Author Summary: Protein degradation controls the abundance of cellular proteins and serves an essential role in protein quality control by eliminating misfolded and damaged proteins. In eukaryotes, most protein degradation occurs in two steps. The ubiquitin system first targets proteins for degradation by attaching ubiquitin to them. The proteasome then selectively binds and degrades ubiquitinated proteins. Understanding how individual genetic differences affect the activity of the proteasome could improve our understanding of the many traits influenced by protein degradation. However, most assays that measure proteasomal protein degradation are not suitable for use in large samples or are affected by changes in the activity of the ubiquitin system. Using yeast, we built reporters that provide high-throughput measurements of proteasome activity independently of the ubiquitin system. We used measurements of proteasome activity from millions of live, single cells to identify regions of the genome with DNA variants that affect proteasomal protein degradation. We identified 15 such regions, showing that proteasome activity is a genetically complex trait. Using genome engineering, we found that one locus contained a variant in the promoter of a proteasome subunit gene that affected the activity of the proteasome towards multiple substrates. Our results demonstrate that individual genetic differences shape proteasome activity and suggest that these differences may contribute to variation in the many traits regulated by protein degradation.

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Figures

Fig 1 Ubiquitin-dependent and -independent proteasomal protein degradation. A. UPS protein degradation resulting from (1) ubiquitin system targeting followed by (2) proteasomal protein degradation. B. Proteins with ubiquitin-independent degrons are directly bound and degraded by the proteasome without ubiquitin system targeting.
Fig 1 Ubiquitin-dependent and -independent proteasomal protein degradation. A. UPS protein degradation resulting from (1) ubiquitin system targeting followed by (2) proteasomal protein degradation. B. Proteins with ubiquitin-independent degrons are directly bound and degraded by the proteasome without ubiquitin system targeting.CC BY · PubMed Central
Fig 2 Design and characterization of proteasome activity reporters. A. Schematic of the production and maturation of a TFT. B. A bar plot created with simulated data showing how differences in a TFT’s degradation rate influence the reporter’s RFP and GFP levels, as well as the -log2 RFP / GFP ratio. C. Diagram of mouse ODC and yeast Rpn4 showing the location of each protein’s ubiquitin-independent degron. “AZB” = antizyme binding site, “AS” = active site, “AD” = transcriptional activation domain, “C2H2” = C2H2 zinc finger DNA binding domain. D. Density plots of proteasome activity from 10,000 cells for each of 8 independent biological replicates per strain per reporter for the indicated strains and TFTs. Thin, opaque lines show individual biological replicates and thicker, transparent lines show the group average for the indicated strains. E. The median from each biological replicate in D. is plotted as a stripchart. t-test p-values are shown for the indicated strain versus BY.
Fig 2 Design and characterization of proteasome activity reporters. A. Schematic of the production and maturation of a TFT. B. A bar plot created with simulated data showing how differences in a TFT’s degradation rate influence the reporter’s RFP and GFP levels, as well as the -log2 RFP / GFP ratio. C. Diagram of mouse ODC and yeast Rpn4 showing the location of each protein’s ubiquitin-independent degron. “AZB” = antizyme binding site, “AS” = active site, “AD” = transcriptional activation domain, “C2H2” = C2H2 zinc finger DNA binding domain. D. Density plots of proteasome activity from 10,000 cells for each of 8 independent biological replicates per strain per reporter for the indicated strains and TFTs. Thin, opaque lines show individual biological replicates and thicker, transparent lines show the group average for the indicated strains. E. The median from each biological replicate in D. is plotted as a stripchart. t-test p-values are shown for the indicated strain versus BY.CC BY · PubMed Central
Fig 3 Mapping genetic influences on proteasome activity using bulk segregant analysis. A. Schematic of the experimental approach. B. / C. Proteasome activity distributions for the ODC TFT (B.) and Rpn4 TFT (C.). Vertical lines show the gates used to collect cells with extreme high or low proteasome activity. D. / E. Backplot of cells collected using the gates in B. / C. onto a scatter plot of GFP and RFP for the ODC (D.) and Rpn4 (E.) TFTs.
Fig 3 Mapping genetic influences on proteasome activity using bulk segregant analysis. A. Schematic of the experimental approach. B. / C. Proteasome activity distributions for the ODC TFT (B.) and Rpn4 TFT (C.). Vertical lines show the gates used to collect cells with extreme high or low proteasome activity. D. / E. Backplot of cells collected using the gates in B. / C. onto a scatter plot of GFP and RFP for the ODC (D.) and Rpn4 (E.) TFTs.CC BY · PubMed Central
Fig 4 Proteasome activity QTLs detected with the ODC and Rpn4 TFTs. A. The line plot shows the loess-smoothed allele frequency difference between the high and low proteasome activity pools across the S. cerevisiae genome for each of two independent biological replicates per reporter. Asterisks denote QTLs, which are allele frequency differences exceeding an empirically-derived LOD score significance threshold (indicated in B.) in each of two independent biological replicates for a given reporter. The horizontal red lines denote an empirically-derived 99.9% quantile of the allele frequency difference. Magenta horizontal lines above pairs of asterisks denote QTLs detected with both TFTs with the same direction of effect, which are termed “overlapping QTLs”. B. As in A., but for the LOD score for proteasome activity QTLs. The red horizontal line denotes the LOD score significance threshold used to call QTLs at a 0.5% FDR.
Fig 4 Proteasome activity QTLs detected with the ODC and Rpn4 TFTs. A. The line plot shows the loess-smoothed allele frequency difference between the high and low proteasome activity pools across the S. cerevisiae genome for each of two independent biological replicates per reporter. Asterisks denote QTLs, which are allele frequency differences exceeding an empirically-derived LOD score significance threshold (indicated in B.) in each of two independent biological replicates for a given reporter. The horizontal red lines denote an empirically-derived 99.9% quantile of the allele frequency difference. Magenta horizontal lines above pairs of asterisks denote QTLs detected with both TFTs with the same direction of effect, which are termed “overlapping QTLs”. B. As in A., but for the LOD score for proteasome activity QTLs. The red horizontal line denotes the LOD score significance threshold used to call QTLs at a 0.5% FDR.CC BY · PubMed Central
Fig 5 Overlap of N-end Rule and proteasome activity QTLs. A. Analysis of QTL overlap for proteasome activity, Arg/N-degron, and Ac/N-degron QTLs. For all pairs of reporters in the indicated reporter sets, we computed the overlap fraction as overlapping QTLs divided by total QTLs (overlapping QTLs plus reporter-specific QTLs). B. Overlap of proteasome activity and N-end Rule QTLs. The plot shows the number, identity, and N-end Rule branch of the N-degron QTLs that overlap proteasome activity QTLs. QTLs on the y axis are ordered first by reporter then by chromosomal position and labeled as in Table 1. N-degrons on the x axis are ordered by the distance of their QTL’s peak position from the peak of the corresponding proteasome activity QTL detected with either the ODC or Rpn4 TFT.
Fig 5 Overlap of N-end Rule and proteasome activity QTLs. A. Analysis of QTL overlap for proteasome activity, Arg/N-degron, and Ac/N-degron QTLs. For all pairs of reporters in the indicated reporter sets, we computed the overlap fraction as overlapping QTLs divided by total QTLs (overlapping QTLs plus reporter-specific QTLs). B. Overlap of proteasome activity and N-end Rule QTLs. The plot shows the number, identity, and N-end Rule branch of the N-degron QTLs that overlap proteasome activity QTLs. QTLs on the y axis are ordered first by reporter then by chromosomal position and labeled as in Table 1. N-degrons on the x axis are ordered by the distance of their QTL’s peak position from the peak of the corresponding proteasome activity QTL detected with either the ODC or Rpn4 TFT.CC BY · PubMed Central
Fig 6 Fine-mapping a causal variant for the chromosome VIIb QTL. A. Genomic interval for the chromosome VIIb QTL. The red box depicts the 95% confidence interval of the chromosome VIIb QTL peak position, which was calculated using the chromosome VIIb QTL intervals from the ODC and N-end Rule TFTs with which the QTL was detected. B. CRISPR-Cas9 was used to engineer strains to contain either the BY or RM allele at RPT6 -175 and the variant’s effect on proteasome activity was measured by flow cytometry. The variant’s effect was tested on strains harboring the ODC and Rpn4 ubiquitin-independent degron TFTs, as well as the proline (Pro), serine (Ser), and threonine (Thr) Ac/N-end TFTs, and the tryptophan (Trp) Arg/N-degron TFT. C. Rpt6 abundance was measured in strains harboring the BY or RM allele at RPT6 -175. D. Alg13 abundance was measured in strains harboring the BY or RM allele at RPT6 -175. In B-D, each point shows the median of 10,000 cells from independent biological replicates following Z-score normalization to the median of the control strain engineered to contain the BY allele at RPT6 -175.
Fig 6 Fine-mapping a causal variant for the chromosome VIIb QTL. A. Genomic interval for the chromosome VIIb QTL. The red box depicts the 95% confidence interval of the chromosome VIIb QTL peak position, which was calculated using the chromosome VIIb QTL intervals from the ODC and N-end Rule TFTs with which the QTL was detected. B. CRISPR-Cas9 was used to engineer strains to contain either the BY or RM allele at RPT6 -175 and the variant’s effect on proteasome activity was measured by flow cytometry. The variant’s effect was tested on strains harboring the ODC and Rpn4 ubiquitin-independent degron TFTs, as well as the proline (Pro), serine (Ser), and threonine (Thr) Ac/N-end TFTs, and the tryptophan (Trp) Arg/N-degron TFT. C. Rpt6 abundance was measured in strains harboring the BY or RM allele at RPT6 -175. D. Alg13 abundance was measured in strains harboring the BY or RM allele at RPT6 -175. In B-D, each point shows the median of 10,000 cells from independent biological replicates following Z-score normalization to the median of the control strain engineered to contain the BY allele at RPT6 -175.CC BY · PubMed Central
Fig 7 Effect of RPT6 overexpression on proteasome activity. A. A BY strain with the chromosomal RPT6 gene tagged with the GFP mNeon was transformed with plasmids containing the RPT6 gene tagged with mNeon and expressed from the native RPT6 promoter or the strong, constitutively active ACT1 promoter. Rpt6 levels were measured by flow cytometry and compared to the same strain transformed with an otherwise identical empty vector control plasmid lacking the mNeon-tagged RPT6 gene. B. BY strains harboring either the ODC or Thr N-degron TFTs were transformed with plasmids expressing RPT6 without an mNeon tag from the native RPT6 or ACT1 promoters. Proteasome activity was measured by flow cytometry and compared to strains harboring an otherwise identical empty vector control plasmid lacking the RPT6 gene. In A. and B. each point represents the median of 10,000 cells after Z-score normalization to the median of the corresponding empty vector control strain.
Fig 7 Effect of RPT6 overexpression on proteasome activity. A. A BY strain with the chromosomal RPT6 gene tagged with the GFP mNeon was transformed with plasmids containing the RPT6 gene tagged with mNeon and expressed from the native RPT6 promoter or the strong, constitutively active ACT1 promoter. Rpt6 levels were measured by flow cytometry and compared to the same strain transformed with an otherwise identical empty vector control plasmid lacking the mNeon-tagged RPT6 gene. B. BY strains harboring either the ODC or Thr N-degron TFTs were transformed with plasmids expressing RPT6 without an mNeon tag from the native RPT6 or ACT1 promoters. Proteasome activity was measured by flow cytometry and compared to strains harboring an otherwise identical empty vector control plasmid lacking the RPT6 gene. In A. and B. each point represents the median of 10,000 cells after Z-score normalization to the median of the corresponding empty vector control strain.CC BY · PubMed Central
Fig 8 Properties of the causal RPT6 -175 causal variant. A. The RPT6 promoter with either the BY or RM allele at RPT6 -175 was scanned for transcription factor binding motifs. The motif plot displays the sequence logo of a Yap1 binding motif created by the RM allele at RPT6 -175. B. Multi-species alignment of the RPT6 promoter with the causal -175 variant highlighted in red. “S. pas” = Saccharomyces pastorianus, “S. par” = Saccharomyces paradoxus, “S. mik” = Saccharomyces mikatae, “S. kud” = Saccharomyces kudriavzevii, “S. bay” = Saccharomyces bayanus, “S. pombe” = Saccharomyces pombe. C. Tree diagram showing the distribution of the RPT6 -175 allele among a panel of 1,011 S. cerevisiae strains. Clades with the RPT6 -175 RM allele are indicated along with its frequency in that clade in parentheses.
Fig 8 Properties of the causal RPT6 -175 causal variant. A. The RPT6 promoter with either the BY or RM allele at RPT6 -175 was scanned for transcription factor binding motifs. The motif plot displays the sequence logo of a Yap1 binding motif created by the RM allele at RPT6 -175. B. Multi-species alignment of the RPT6 promoter with the causal -175 variant highlighted in red. “S. pas” = Saccharomyces pastorianus, “S. par” = Saccharomyces paradoxus, “S. mik” = Saccharomyces mikatae, “S. kud” = Saccharomyces kudriavzevii, “S. bay” = Saccharomyces bayanus, “S. pombe” = Saccharomyces pombe. C. Tree diagram showing the distribution of the RPT6 -175 allele among a panel of 1,011 S. cerevisiae strains. Clades with the RPT6 -175 RM allele are indicated along with its frequency in that clade in parentheses.CC BY · PubMed Central

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Metadata

Authors
Collins, M. A., Avery, R. R., & Albert, F. W.
Affiliations
University of Minnesota (US)
Corresponding author
  • Collins, Mahlon A.
  • Albert, Frank W.
ORCID
Venue
PLoS Genetics
Publisher
openRxiv
Published
2021-11-24
Volume
19
Issue
5
Pages
e1010734-e1010734
ISSN
1553-7390 (ISSN-L)
DOI
10.1101/2021.11.23.469794
PMC
PMC10174532
OpenAlex
W4367597147
Cited by
11
References
1
Citations by year
2026: 4 · 2025: 6 · 2022: 1
FWCI
0.89
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • PLoS Genetics (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • University of Minnesota
  • National Institutes of Health (1f32gm128302-01a1)
  • National Institute of General Medical Sciences (F32 GM128302, R35GM124676)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Ubiquitin and proteasome pathways
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Cell Biology › Autophagy in Disease and Therapy
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Fungal and yeast genetics research
Keywords
proteasome activity, natural genetic variation, protein degradation, ubiquitin system, substrate specificity, Saccharomyces cerevisiae, Rpt6, 19S regulatory particle, genetic architecture, promoter polymorphism, gene mapping
MeSH terms
Saccharomyces cerevisiae, Genetic Variation, Ubiquitin, Saccharomyces cerevisiae Proteins, Proteasome Endopeptidase Complex, Proteolysis

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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