19 research outputs found

    Genetic associations at 53 loci highlight cell types and biological pathways relevant for kidney function.

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    Reduced glomerular filtration rate defines chronic kidney disease and is associated with cardiovascular and all-cause mortality. We conducted a meta-analysis of genome-wide association studies for estimated glomerular filtration rate (eGFR), combining data across 133,413 individuals with replication in up to 42,166 individuals. We identify 24 new and confirm 29 previously identified loci. Of these 53 loci, 19 associate with eGFR among individuals with diabetes. Using bioinformatics, we show that identified genes at eGFR loci are enriched for expression in kidney tissues and in pathways relevant for kidney development and transmembrane transporter activity, kidney structure, and regulation of glucose metabolism. Chromatin state mapping and DNase I hypersensitivity analyses across adult tissues demonstrate preferential mapping of associated variants to regulatory regions in kidney but not extra-renal tissues. These findings suggest that genetic determinants of eGFR are mediated largely through direct effects within the kidney and highlight important cell types and biological pathways

    Create and preserve: Proteostasis in development and aging is governed by Cdc48/p97/VCP

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    The AAA-ATPase Cdc48 (also called p97 or VCP) acts as a key regulator in proteolytic pathways, coordinating recruitment and targeting of substrate proteins to the 26S proteasome or lysosomal degradation. However, in contrast to the well-known function in ubiquitin-dependent cellular processes, the physiological relevance of Cdc48 in organismic development and maintenance of protein homeostasis is less understood. Therefore, studies on multicellular model organisms help to decipher how Cdc48-dependent proteolysis is regulated in time and space to meet developmental requirements. Given the importance of developmental regulation and tissue maintenance, defects in Cdc48 activity have been linked to several human pathologies including protein aggregation diseases. Thus, addressing the underlying disease mechanisms not only contributes to our understanding on the organism-wide function of Cdc48 but also facilitates the design of specific medical therapies. In this review, we will portray the role of Cdc48 in the context of multicellular organisms, pointing out its importance for developmental processes, tissue surveillance, and disease prevention. This article is part of a Special Issue entitled: Ubiquitin-Proteasome System. Guest Editors: Thomas Sommer and Dieter H. Wolf. (C) 2013 Elsevier B.V. All rights reserved

    Ring of Change: CDC48/p97 Drives Protein Dynamics at Chromatin

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    The dynamic composition of proteins associated with nuclear DNA is a fundamental property of chromosome biology. In the chromatin compartment dedicated protein complexes govern the accurate synthesis and repair of the genomic information and define the state of DNA compaction in vital cellular processes such as chromosome segregation or transcription. Unscheduled or faulty association of protein complexes with DNA has detrimental consequences on genome integrity. Consequently, the association of protein complexes with DNA is remarkably dynamic and can respond rapidly to cellular signaling events, which requires tight spatiotemporal control. In this context, the ring-like AAA+ ATPase CDC48/p97 emerges as a key regulator of protein complexes that are marked with ubiquitin or SUMO. Mechanistically, CDC48/p97 functions as a segregase facilitating the extraction of substrate proteins from the chromatin. As such, CDC48/p97 drives molecular reactions either by directed disassembly or rearrangement of chromatin-bound protein complexes. The importance of this mechanism is reflected by human pathologies linked to p97 mutations, including neurodegenerative disorders, oncogenesis, and premature aging. This review focuses on the recent insights into molecular mechanisms that determine CDC48/p97 function in the chromatin environment, which is particularly relevant for cancer and aging research

    Mechanism and function of DNA replication-independent DNA-protein crosslink repair via the SUMO-RNF4 pathway

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    DNA‐protein crosslinks (DPCs) obstruct essential DNA transactions, posing a serious threat to genome stability and functionality. DPCs are proteolytically processed in a ubiquitin‐ and DNA replication‐dependent manner by SPRTN and the proteasome but can also be resolved via targeted SUMOylation. However, the mechanistic basis of SUMO‐mediated DPC resolution and its interplay with replication‐coupled DPC repair remain unclear. Here, we show that the SUMO‐targeted ubiquitin ligase RNF4 defines a major pathway for ubiquitylation and proteasomal clearance of SUMOylated DPCs in the absence of DNA replication. Importantly, SUMO modifications of DPCs neither stimulate nor inhibit their rapid DNA replication‐coupled proteolysis. Instead, DPC SUMOylation provides a critical salvage mechanism to remove DPCs formed after DNA replication, as DPCs on duplex DNA do not activate interphase DNA damage checkpoints. Consequently, in the absence of the SUMO‐RNF4 pathway cells are able to enter mitosis with a high load of unresolved DPCs, leading to defective chromosome segregation and cell death. Collectively, these findings provide mechanistic insights into SUMO‐driven pathways underlying replication‐independent DPC resolution and highlight their critical importance in maintaining chromosome stability and cellular fitness

    Ubiquitin C-Terminal Hydrolase-L1 Potentiates Cancer Chemosensitivity by Stabilizing NOXA

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    The BH3-only protein NOXA represents one of the critical mediators of DNA-damage-induced cell death. In particular, its involvement in cellular responses to cancer chemotherapy is increasingly evident. Here, we identify a strategy of cancer cells to escape genotoxic chemotherapy by increasing proteasomal degradation of NOXA. We show that the deubiquitylating enzyme UCH-L1 is a key regulator of NOXA turnover, which protects NOXA from proteasomal degradation by removing Lys48-linked polyubiquitin chains. In the majority of tumors from patients with melanoma or colorectal cancer suffering from high rates of chemoresistance, NOXA fails to accumulate because UCH-L1 expression is epigenetically silenced. Whereas UCH-L1/NOXA-positive tumor samples exhibit increased sensitivity to genotoxic chemotherapy, downregulation of UCH-L1 or inhibition of its deubiquitylase activity resulted in reduced NOXA stability and resistance to genotoxic chemotherapy in both human and C. elegans cells. Our data identify the UCH-L1/NOXA interaction as a therapeutic target for overcoming cancer chemoresistance

    Machine-learning analysis of cross-study samples according to the gut microbiome in 12 infant cohorts

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    Combining and comparing microbiome data from distinct infant cohorts has been challenging because such data are inherently multidimensional and complex. Here, we used an ensemble of machine-learning (ML) models and studied 16S rRNA amplicon sequencing data from 4,099 gut microbiome samples representing 12 prospectively collected infant cohorts. We chose the childbirth delivery mode as a starting point for such analysis because it has previously been associated with alterations in the gut microbiome in infants. In cross-study ensemble models, Bacteroides was the most important feature in all machine-learning models. The predictive capacity by taxonomy varied with age. At the age of 1-2 months, gut microbiome data were able to predict delivery mode with an area under the curve of 0.72 to 0.83. In contrast, ML models trained on taxa were not able to differentiate between the modes of delivery, in any of the cohorts, when the infants were between 3 and 12 months of age. Moreover, no ML model, alternately trained on the functional pathways of the infant gut microbiome, could consistently predict mode of delivery at any infant age. This study shows that infant gut microbiome data sets can be effectively combined with the application of ML analysis across different study populations.IMPORTANCEThere are challenges in merging microbiome data from diverse research groups due to the intricate and multifaceted nature of such data. To address this, we utilized a combination of machine-learning (ML) models to analyze 16S sequencing data from a substantial set of gut microbiome samples, sourced from 12 distinct infant cohorts that were gathered prospectively. Our initial focus was on the mode of delivery due to its prior association with changes in infant gut microbiomes. Through ML analysis, we demonstrated the effective merging and comparison of various gut microbiome data sets, facilitating the identification of robust microbiome biomarkers applicable across varied study populations.Peer reviewe
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