Iron insufficiency compromises motor neurons and their mitochondrial function in Irp2-null mice
Article - Full Text
Supplemental File 1
Supplemental File 2
Supplemental File 3
Supplemental File 4
Supplemental File 5
Supplemental File 6
AuthorsJeong, Suh Young
Crooks, Daniel R.
Ghosh, Manik C.
Mitchell, James B.
Rouault, Tracey A.
KAUST DepartmentImaging and Characterization Core Lab
Permanent link to this recordhttp://hdl.handle.net/10754/325294
MetadataShow full item record
AbstractGenetic ablation of Iron Regulatory Protein 2 (Irp2, Ireb2), which post-transcriptionally regulates iron metabolism genes, causes a gait disorder in mice that progresses to hind-limb paralysis. Here we have demonstrated that misregulation of iron metabolism from loss of Irp2 causes lower motor neuronal degeneration with significant spinal cord axonopathy. Mitochondria in the lumbar spinal cord showed significantly decreased Complex I and II activities, and abnormal morphology. Lower motor neurons appeared to be the most adversely affected neurons, and we show that functional iron starvation due to misregulation of iron import and storage proteins, including transferrin receptor 1 and ferritin, may have a causal role in disease. We demonstrated that two therapeutic approaches were beneficial for motor neuron survival. First, we activated a homologous protein, IRP1, by oral Tempol treatment and found that axons were partially spared from degeneration. Secondly, we genetically decreased expression of the iron storage protein, ferritin, to diminish functional iron starvation. These data suggest that functional iron deficiency may constitute a previously unrecognized molecular basis for degeneration of motor neurons in mice.
CitationJeong SY, Crooks DR, Wilson-Ollivierre H, Ghosh MC, Sougrat R, et al. (2011) Iron Insufficiency Compromises Motor Neurons and Their Mitochondrial Function in Irp2-Null Mice. PLoS ONE 6: e25404. doi:10.1371/journal.pone.0025404.
PublisherPublic Library of Science (PLoS)
PubMed Central IDPMC3189198
- Tempol-mediated activation of latent iron regulatory protein activity prevents symptoms of neurodegenerative disease in IRP2 knockout mice.
- Authors: Ghosh MC, Tong WH, Zhang D, Ollivierre-Wilson H, Singh A, Krishna MC, Mitchell JB, Rouault TA
- Issue date: 2008 Aug 19
- Complete loss of iron regulatory proteins 1 and 2 prevents viability of murine zygotes beyond the blastocyst stage of embryonic development.
- Authors: Smith SR, Ghosh MC, Ollivierre-Wilson H, Hang Tong W, Rouault TA
- Issue date: 2006 Mar-Apr
- Iron regulatory protein 1 outcompetes iron regulatory protein 2 in regulating cellular iron homeostasis in response to nitric oxide.
- Authors: Styś A, Galy B, Starzyński RR, Smuda E, Drapier JC, Lipiński P, Bouton C
- Issue date: 2011 Jul 1
- Iron regulatory protein deficiency compromises mitochondrial function in murine embryonic fibroblasts.
- Authors: Li H, Zhao H, Hao S, Shang L, Wu J, Song C, Meyron-Holtz EG, Qiao T, Li K
- Issue date: 2018 Mar 23
- Iron regulatory protein-2 knockout increases perihematomal ferritin expression and cell viability after intracerebral hemorrhage.
- Authors: Chen M, Awe OO, Chen-Roetling J, Regan RF
- Issue date: 2010 Jun 14
Showing items related by title, author, creator and subject.
Dissecting JACKDAW transcriptional regulatory module and protein– protein interaction domainsAlidrissi, Louai (2019-11) [Thesis]
Advisor: Blilou, Ikram
Committee members: Merzaban, Jasmeen; Al-Babili, SalimMany biological processes are regulated via the action of interacting transcription factors. Together, these proteins form a complex regulatory network that will lead to different outcomes according to the DNA context of their target. The transcriptional regulator JACKDAW (JKD) together with the transcription factors SHR (SHORT-ROOT) and (SCARECROW) form a regulatory module that control a variety cell type specific targets to maintain the stem cell niche and regulate asymmetric cell division in the Arabidopsis root meristem. JKD functions is required to restrict the mobile transcription factor SHR to a single layer the endodermis by nuclear retention through association with SCR forming a ternary complex. Unpublished data from a yeast 2 hybrid screen indicated that JKD forms complexes with other transcriptional regulators involved either in growth or defense pathways, but the nature and the function of these interactions are yet to be elucidated. Here we validated these interactions using Yeast 2-Hybrid and Bimolecular fluorescence complementation (BiFC), which are widely used methods to study protein-protein interaction and can potentially provide valuable information in protein-complex composition. We also exploited these two approaches to determine interaction domains of JKD’s. However, the results from the Y2-H and BiFC assays showed inconsistent data. This hindered our ability to conclusively define the interacting JKD variants with various plant-specific transcriptional regulators. As JKD acts as a transcriptional regulator we tested JKD truncated variants on its target promoter pMYC2 using Dual-luciferase reporter assay in a plant-based expression. Our data indicated the significance of its ZF1 motif in mediating DNA binding. To assess whether JKD regulates its targets or in association with SCR and SHR we performed the same assay in a mammalian-based system to avoid plant transcriptional regulation. Our data indicate that based on the target DNA sequence, each of these TFs can either act either as an activator or repressor.
Characterization of structure and function of the interaction between the HIV-1 Nef protein and the p85 regulatory subunit of the phosphatidylinositol 3 kinase (PI3K).Aljedani, Safia Salim Eid (2018-01-24) [Poster]
ProDis-ContSHC: Learning protein dissimilarity measures and hierarchical context coherently for protein-protein comparison in protein database retrievalWang, Jim Jing-Yan; Gao, Xin; Wang, Quanquan; Li, Yongping (BMC Bioinformatics, Springer Nature, 2012-05-08) [Article]Background: The need to retrieve or classify protein molecules using structure or sequence-based similarity measures underlies a wide range of biomedical applications. Traditional protein search methods rely on a pairwise dissimilarity/similarity measure for comparing a pair of proteins. This kind of pairwise measures suffer from the limitation of neglecting the distribution of other proteins and thus cannot satisfy the need for high accuracy of the retrieval systems. Recent work in the machine learning community has shown that exploiting the global structure of the database and learning the contextual dissimilarity/similarity measures can improve the retrieval performance significantly. However, most existing contextual dissimilarity/similarity learning algorithms work in an unsupervised manner, which does not utilize the information of the known class labels of proteins in the database.Results: In this paper, we propose a novel protein-protein dissimilarity learning algorithm, ProDis-ContSHC. ProDis-ContSHC regularizes an existing dissimilarity measure dij by considering the contextual information of the proteins. The context of a protein is defined by its neighboring proteins. The basic idea is, for a pair of proteins (i, j), if their context N (i) and N (j) is similar to each other, the two proteins should also have a high similarity. We implement this idea by regularizing dij by a factor learned from the context N (i) and N (j). Moreover, we divide the context to hierarchial sub-context and get the contextual dissimilarity vector for each protein pair. Using the class label information of the proteins, we select the relevant (a pair of proteins that has the same class labels) and irrelevant (with different labels) protein pairs, and train an SVM model to distinguish between their contextual dissimilarity vectors. The SVM model is further used to learn a supervised regularizing factor. Finally, with the new Supervised learned Dissimilarity measure, we update the Protein Hierarchial Context Coherently in an iterative algorithm--ProDis-ContSHC.We test the performance of ProDis-ContSHC on two benchmark sets, i.e., the ASTRAL 1.73 database and the FSSP/DALI database. Experimental results demonstrate that plugging our supervised contextual dissimilarity measures into the retrieval systems significantly outperforms the context-free dissimilarity/similarity measures and other unsupervised contextual dissimilarity measures that do not use the class label information.Conclusions: Using the contextual proteins with their class labels in the database, we can improve the accuracy of the pairwise dissimilarity/similarity measures dramatically for the protein retrieval tasks. In this work, for the first time, we propose the idea of supervised contextual dissimilarity learning, resulting in the ProDis-ContSHC algorithm. Among different contextual dissimilarity learning approaches that can be used to compare a pair of proteins, ProDis-ContSHC provides the highest accuracy. Finally, ProDis-ContSHC compares favorably with other methods reported in the recent literature. 2012 Wang et al.; licensee BioMed Central Ltd.