Dataset Information

Mutations in GDAP1 Influence Structure and Function of the Trans-Golgi Network.

ABSTRACT: Charcot-Marie-Tooth disease (CMT) is a heritable neurodegenerative disease that displays great genetic heterogeneity. The genes and mutations that underlie this heterogeneity have been extensively characterized by molecular genetics. However, the molecular pathogenesis of the vast majority of CMT subtypes remains terra incognita. Any attempts to perform experimental therapy for CMT disease are limited by a lack of understanding of the pathogenesis at a molecular level. In this study, we aim to identify the molecular pathways that are disturbed by mutations in the gene encoding GDAP1 using both yeast and human cell, based models of CMT-GDAP1 disease. We found that some mutations in GDAP1 led to a reduced expression of the GDAP1 protein and resulted in a selective disruption of the Golgi apparatus. These structural alterations are accompanied by functional disturbances within the Golgi. We screened over 1500 drugs that are available on the market using our yeast-based CMT-GDAP1 model. Drugs were identified that had both positive and negative effects on cell phenotypes. To the best of our knowledge, this study is the first report of the Golgi apparatus playing a role in the pathology of CMT disorders. The drugs we identified, using our yeast-based CMT-GDAP1 model, may be further used in translational research.

SUBMITTER: Binieda K

PROVIDER: S-EPMC7831947 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

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Mutations in <i>GDAP1</i> Influence Structure and Function of the Trans-Golgi Network.

Binięda Katarzyna K Rzepnikowska Weronika W Kolakowski Damian D Kaminska Joanna J Szczepankiewicz Andrzej Antoni AA Nieznańska Hanna H Kochański Andrzej A Kabzińska Dagmara D

International journal of molecular sciences 20210118 2

Charcot-Marie-Tooth disease (CMT) is a heritable neurodegenerative disease that displays great genetic heterogeneity. The genes and mutations that underlie this heterogeneity have been extensively characterized by molecular genetics. However, the molecular pathogenesis of the vast majority of CMT subtypes remains terra incognita. Any attempts to perform experimental therapy for CMT disease are limited by a lack of understanding of the pathogenesis at a molecular level. In this study, we aim to i ...[more]

PMID: 33477664

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Project description:Knowledge of protein subcellular localization assists in the elucidation of protein function and understanding of different biological mechanisms that occur at discrete subcellular niches. Organelle-centric proteomics enables localization of thousands of proteins simultaneously. Although such techniques have successfully allowed organelle protein catalogues to be achieved, they rely on the purification or significant enrichment of the organelle of interest, which is not achievable for many organelles. Incomplete separation of organelles leads to false discoveries, with erroneous assignments. Proteomics methods that measure the distribution patterns of specific organelle markers along density gradients are able to assign proteins of unknown localization based on comigration with known organelle markers, without the need for organelle purification. These methods are greatly enhanced when coupled to sophisticated computational tools. Here we apply and compare multiple approaches to establish a high-confidence data set of Arabidopsis root tissue trans-Golgi network (TGN) proteins. The method employed involves immunoisolations of the TGN, coupled to probability-based organelle proteomics techniques. Specifically, the technique known as LOPIT (localization of organelle protein by isotope tagging), couples density centrifugation with quantitative mass-spectometry-based proteomics using isobaric labeling and targeted methods with semisupervised machine learning methods. We demonstrate that while the immunoisolation method gives rise to a significant data set, the approach is unable to distinguish cargo proteins and persistent contaminants from full-time residents of the TGN. The LOPIT approach, however, returns information about many subcellular niches simultaneously and the steady-state location of proteins. Importantly, therefore, it is able to dissect proteins present in more than one organelle and cargo proteins en route to other cellular destinations from proteins whose steady-state location favors the TGN. Using this approach, we present a robust list of Arabidopsis TGN proteins.

Dataset Information

Mutations in GDAP1 Influence Structure and Function of the Trans-Golgi Network.

Publications

Mutations in <i>GDAP1</i> Influence Structure and Function of the Trans-Golgi Network.

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