Dataset Information

The human GID complex engages two independent modules for substrate recruitment.

ABSTRACT: The human GID (hGID) complex is a conserved E3 ubiquitin ligase regulating diverse biological processes, including glucose metabolism and cell cycle progression. However, the biochemical function and substrate recognition of the multi-subunit complex remain poorly understood. Using biochemical assays, cross-linking mass spectrometry, and cryo-electron microscopy, we show that hGID engages two distinct modules for substrate recruitment, dependent on either WDR26 or GID4. WDR26 and RanBP9 cooperate to ubiquitinate HBP1 in vitro, while GID4 is dispensable for this reaction. In contrast, GID4 functions as an adaptor for the substrate ZMYND19, which surprisingly lacks a Pro/N-end degron. GID4 substrate binding and ligase activity is regulated by ARMC8α, while the shorter ARMC8β isoform assembles into a stable hGID complex that is unable to recruit GID4. Cryo-EM reconstructions of these hGID complexes reveal the localization of WDR26 within a ring-like, tetrameric architecture and suggest that GID4 and WDR26/Gid7 utilize different, non-overlapping binding sites. Together, these data advance our mechanistic understanding of how the hGID complex recruits cognate substrates and provides insights into the regulation of its E3 ligase activity.

SUBMITTER: Mohamed WI

PROVIDER: S-EPMC8567238 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature

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The human GID complex engages two independent modules for substrate recruitment.

Mohamed Weaam I WI Park Sophia L SL Rabl Julius J Leitner Alexander A Boehringer Daniel D Peter Matthias M

EMBO reports 20211014 11

The human GID (hGID) complex is a conserved E3 ubiquitin ligase regulating diverse biological processes, including glucose metabolism and cell cycle progression. However, the biochemical function and substrate recognition of the multi-subunit complex remain poorly understood. Using biochemical assays, cross-linking mass spectrometry, and cryo-electron microscopy, we show that hGID engages two distinct modules for substrate recruitment, dependent on either WDR26 or GID4. WDR26 and RanBP9 cooperat ...[more]

PMID: 34647674

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Project description:BACKGROUND:Analyzing the human transcriptome is crucial in advancing precision medicine, and the plethora of over half a million human microarray samples in the Gene Expression Omnibus (GEO) has enabled us to better characterize biological processes at the molecular level. However, transcriptomic analysis is challenging because the data is inherently noisy and high-dimensional. Gene set analysis is currently widely used to alleviate the issue of high dimensionality, but the user-defined choice of gene sets can introduce biasness in results. In this paper, we advocate the use of a fixed set of transcriptomic modules for such analysis. We apply independent component analysis to the large collection of microarray data in GEO in order to discover reproducible transcriptomic modules that can be used as features for machine learning. We evaluate the usability of these modules across six studies, and demonstrate (1) their usage as features for sample classification, and also their robustness in dealing with small training sets, (2) their regularization of data when clustering samples and (3) the biological relevancy of differentially expressed features. RESULTS:We identified 139 reproducible transcriptomic modules, which we term fundamental components (FCs). In studies with less than 50 samples, FC-space classification model outperformed their gene-space counterparts, with higher sensitivity (p <?0.01). The models also had higher accuracy and negative predictive value (p <?0.01) for small data sets (less than 30 samples). Additionally, we observed a reduction in batch effects when data is clustered in the FC-space. Finally, we found that differentially expressed FCs mapped to GO terms that were also identified via traditional gene-based approaches. CONCLUSIONS:The 139 FCs provide biologically-relevant summarization of transcriptomic data, and their performance in low sample settings suggest that they should be employed in such studies in order to harness the data efficiently.

Dataset Information

The human GID complex engages two independent modules for substrate recruitment.

Publications

The human GID complex engages two independent modules for substrate recruitment.

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