Assembly and function of branched ubiquitin chains

Kolla, SriDurgaDevi; Ye, Mengchen; Mark, Kevin G.; Rapé, Michael

doi:10.1016/j.tibs.2022.04.003

Cited by 64 publications

(43 citation statements)

References 127 publications

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“…UBR5 possesses a UBA domain that binds ubiquitin and a HECT-domain that catalyzes ubiquitin transfer (Kim et al, 2021). These domains allow UBR5 to build K11/K48- or K63/K48-branched ubiquitin chains that elicit efficient proteasomal degradation (Kolla et al, 2022; Ohtake et al, 2018; Yau et al, 2017). To test if UBR5 induces the turnover of proteins that cooperate with c-MYC, we fused each interactor and several of their related proteins to GFP and co-expressed them with mCherry that was under control of an internal ribosome entry site.…”

Section: Resultsmentioning

confidence: 99%

“…Using MCRS1 as our model, we asked whether UBR5 produces ubiquitin chains that can trigger protein degradation through the 26S proteasome. Such conjugates include canonical K48-linked chains or heterotypic K11/K48- and K63/K48-branched conjugates (Kolla et al, 2022; Yau and Rape, 2016). By employing ubiquitin variants that lacked specific Lys residues, we found that UBR5 showed a preference for synthesizing K48-linkages, but chain formation was also reduced if Lys63 of ubiquitin was mutated ( Figure 3C ).…”

Section: Resultsmentioning

confidence: 99%

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Orphan quality control shapes network dynamics and gene expression

Mark

Kolla

Garshott

et al. 2022

Preprint

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All eukaryotes require intricate protein networks to translate developmental signals into accurate cell fate decisions. Mutations that disturb crucial interactions between network components often result in disease, but how the composition and dynamics of complex networks are established is unknown. Here, we identify the tumor suppressor E3 ligase UBR5 as a quality control enzyme that helps degrade unpaired subunits of multiple transcription factors that operate within a single network. By constantly turning over orphan subunits, UBR5 forces cells to continuously replenish network components through new protein synthesis. The resulting cycles of transcription factor synthesis and degradation allow cells to effectively execute the gene expression program, while remaining susceptible to environmental signals. We conclude that orphan quality control plays an essential role in establishing the dynamics of protein networks, which may explain the conserved need for protein degradation in transcription and offers unique opportunities to modulate gene expression in disease.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Orphan quality control shapes network dynamics and gene expression

Mark

Kolla

Garshott

et al. 2022

Preprint

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“…In addition, HUWE1 and TRIP12, another pleotropic HECT-domain ligase, were demonstrated to target a UFD reporter substrate in human cells, arguing that HUWE1 may amplify ubiquitin chains on substrates previously ubiquitylated by other ligases (Poulsen et al, 2012). Binding preexisting ubiquitin chains may also allow HUWE1 to build branched ubiquitin chains on substrates to accelerate proteasome targeting, a property that has been described for HUWE1, TRIP12 and the HUWE1 ortholog Tom1 (Kaiho-Soma et al, 2021; Kats et al , 2022; Kolla et al, 2022; Ohtake et al , 2016; Yau et al, 2017). This possible E4 mechanism for HUWE1 is also consistent with the notion that HUWE1 may not have many dedicated substrates.…”

Section: Discussionmentioning

confidence: 92%

HAPSTR1 localizes HUWE1 to the nucleus to limit stress signaling pathways

Monda

Hunkeler

et al. 2023

Preprint

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HUWE1 is a large, enigmatic HECT domain ubiquitin ligase implicated in the regulation of diverse pathways including DNA repair, apoptosis, and differentiation. How HUWE1 engages its structurally diverse substrates and how HUWE1 activity is regulated are unknown. Using unbiased quantitative proteomics, we identify C16orf72/HAPSTR1 as a dedicated HUWE1 substrate despite HUWE1 targeting substrates in a largely cell type-specific manner. The established physical and genetic interactions between HUWE1 and C16orf72/HAPSTR1 suggest that HAPSTR1 positively regulates HUWE1 function. Here, we show that HAPSTR1 is both a HUWE1 substrate and is required for HUWE1 nuclear localization to facilitate HUWE1 nuclear substrate targeting. HUWE1 or HAPSTR1 loss of function triggers a broad transcriptional stress response. We show that nuclear HUWE1 is both critical for modulating stress signaling pathways, which include p53 and NF-κB-mediated signaling and required for cell proliferation. Combined, our results define a role for HAPSTR1 in gating critical nuclear HUWE1 functions.

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“…In recent years, a series of genetic diseases, including DCM, caused by abnormal nucleotide metabolism have been found [41]. Ubiquitination refers to the process in which ubiquitins (a class of low molecular weight proteins) classify proteins in cells under the action of a series of special enzymes, select target proteins from them, and modify the target proteins speci cally [42]. DCM are associated with cardiac remodeling, where the ubiquitin-proteasome system (UPS) holds a central role.…”

Section: Discussionmentioning

confidence: 99%

Integrated genomic analysis defines molecular subgroups in dilated cardiomyopathy and identifies novel biomarkers based on machine learning methods

2022

Preprint

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Aim As the most common cardiomyopathy, dilated cardiomyopathy (DCM) often leads to progressive heart failure and sudden cardiac death. This study was designed to investigate the molecular subgroups of DCM. Methods Three datasets of DCM were downloaded from GEO database (GSE17800, GSE79962 and GSE3585). After log2-transformation and background correction with “limma” package in R software, the three datasets were merged into a metadata cohort. The consensus clustering was conducted by the “Consensus Cluster Plus” package to uncover the molecular subgroups of DCM. Moreover, clinical characteristics of different molecular subgroups were compared in detail. We also adopted Weighted gene co-expression network analysis (WGCNA) analysis based on subgroup-specific signatures of gene expression profiles to further explore the specific gene modules of each molecular subgroup and its biological function. Two machine learning methods of LASSO regression algorithm and SVM-RFE algorithm was used to screen out the genetic biomarkers, of which the discriminative ability of molecular subgroups was evaluated by receiver operating characteristic (ROC) curve. Results Based on the gene expression profiles, heart tissue samples from patients with DCM were clustered into three molecular subgroups. No statistical difference was found in age, body mass index (BMI) and left ventricular internal diameter at end-diastole (LVIDD) among three molecular subgroups. However, the results of left ventricular ejection fraction (LVEF) statistics showed that patients from subgroup 2 had a worse condition than the other group. We found that some of the gene modules (pink, black and grey) in WGCNA analysis were significantly related to cardiac function, and each molecular subgroup had its specific gene modules functions in modulating occurrence and progression of DCM. LASSO regression algorithm and SVM-RFE algorithm was used to further screen out genetic biomarkers of molecular subgroup 2, including TCEAL4, ISG15, RWDD1, ALG5, MRPL20, JTB and LITAF. The results of ROC curves showed that all of the genetic biomarkers had favorable discriminative effectiveness. Conclusions Patients from different molecular subgroups have their unique gene expression patterns and different clinical characteristics. More personalized treatment under the guidance of gene expression patterns should be realized.

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Assembly and function of branched ubiquitin chains

Cited by 64 publications

References 127 publications

Orphan quality control shapes network dynamics and gene expression

Orphan quality control shapes network dynamics and gene expression

HAPSTR1 localizes HUWE1 to the nucleus to limit stress signaling pathways

Integrated genomic analysis defines molecular subgroups in dilated cardiomyopathy and identifies novel biomarkers based on machine learning methods

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