Dataset Information

The Dsc ubiquitin ligase complex identifies transmembrane degrons to degrade orphaned proteins at the Golgi

ABSTRACT: We quantified the proteome of Dsc complex mutants (tul1gld1) and ESCRT mutants (vps4). Therefore we used stable isotope labeling with amino acids in cell culture (SILAC) to identify changes in the membrane proteome of tul1gld1orvps4 cells relative to WT cells or gld1 cells (labeled with “heavy” [13C6,15N2]-L-lysine).

INSTRUMENT(S):

ORGANISM(S): Candida Albicans (yeast)

SUBMITTER: Leopold Kremser

LAB HEAD: David Teis

PROVIDER: PXD055553 | Pride | 2025-05-07

REPOSITORIES: Pride

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Publications

The Dsc ubiquitin ligase complex identifies transmembrane degrons to degrade orphaned proteins at the Golgi.

Weyer Yannick Y Schwabl Sinead I SI Tang Xuechen X Purwar Astha A Siegmann Konstantin K Ruepp Angela A Dunzendorfer-Matt Theresia T Widerin Michael A MA Niedrist Veronika V Mutsters Noa J M NJM Tettamanti Maria G MG Weys Sabine S Sarg Bettina B Kremser Leopold L Liedl Klaus R KR Schmidt Oliver O Teis David D

Nature communications 20241026 1

The Golgi apparatus is essential for protein sorting, yet its quality control mechanisms are poorly understood. Here we show that the Dsc ubiquitin ligase complex uses its rhomboid pseudo-protease subunit, Dsc2, to assess the hydrophobic length of α-helical transmembrane domains (TMDs) at the Golgi. Thereby the Dsc complex likely interacts with orphaned ER and Golgi proteins that have shorter TMDs and ubiquitinates them for targeted degradation. Some Dsc substrates will be extracted by Cdc48 for ...[more]

PMID: 39461958

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Project description:This study is a quantitative trait linkage mapping study targeted at gene-environment interaction. A family of yeast derived from BY4716 and RM11-1a was grown in two carbon sources (glucose and ethanol) and expression profiled. The analysis revealed how the influence of genetic factors changes in different conditions. Abstract: The effects of genetic variants on phenotypic traits often depend on environmental and physiological conditions, but such gene–environment interactions are poorly understood. Recently developed approaches that treat transcript abundances of thousands of genes as quantitative traits offer the opportunity to broadly characterize the architecture of gene–environment interactions. We examined the genetic and molecular basis of variation in gene expression between two yeast strains (BY and RM) grown in two different conditions (glucose and ethanol carbon sources). We observed that most transcripts vary by strain and condition, with 2,996, 3,448, and 2,037 transcripts showing significant strain, condition, and strain–condition interaction effects, respectively. We expression profiled over 100 segregants derived from a cross between RM and BY in both growth conditions, and identified 1,555 linkages for 1,382 transcripts that show significant gene–environment interaction. At the locus level, local linkages, which usually correspond to polymorphisms in cis-regulatory elements, tend to be more stable across conditions, such that they are more likely to show the same effect or the same direction of effect across conditions. Distant linkages, which usually correspond to polymorphisms influencing trans-acting factors, are more condition-dependent, and often show effects in different directions in the two conditions. We characterized a locus that influences expression of many growth- related transcripts, and showed that the majority of the variation is explained by polymorphism in the gene IRA2. The RM allele of IRA2 appears to inhibit Ras/PKA signaling more strongly than the BY allele, and has undergone a change in selective pressure. Our results provide a broad overview of the genetic architecture of gene–environment interactions, as well as a detailed molecular example, and lead to key insights into how the effects of different classes of regulatory variants are modulated by the environment. These observations will guide the design of studies aimed at understanding the genetic basis of complex traits. Keywords: QTL This data set includes 24 parental arrays (6 of each parent in each condition, 218 segregant arrays (109 in both conditions), and 4 allele replacement arrays (IRA2 replacements in both backgrounds in both conditions). All arrays are from an unique biological sample, and many are randomized for dye (no dye-swaps on the same RNA sample). For the 24 parental arrays, there are three of each strain*condition*dye combination. The segregant arrays were randomized for dye. The replacement arrays are all of the Cy5 = experimental, Cy3 = reference type. All arrays are hybridized to a common reference consisting of an equal portions of RNA samples. from BY and RM grown in glucose and ethanol. Analysis was performed using default settings in Agilent Feature Extractor software versions 8.0-9.5. Spots were excluded if their intensity was below 350 or if they were flagged as non-uniformity outliers. Transcripts were considered good if they were present in all parental arrays (for the parental analysis) or 80% of all arrays in each condition for the segregant analysis.

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	KSE_0011_Fr4.raw	Raw
	KSE_0011_Fr5.raw	Raw
	KSE_0011_Fr6.raw	Raw