Cdh1 degradation is mediated by APC/C-Cdh1 and SCF-Cdc4 in budding yeast

APC/C-Cdh1-targeted substrates as potential therapies for Alzheimer's disease

Alzheimer's disease (AD) is the most prevalent neurodegenerative disorder and the main cause of dementia in the elderly. The disease has a high impact on individuals and their families and represents a growing public health and socio-economic burden. Despite this, there is no effective treatment options to cure or modify the disease progression, highlighting the need to identify new therapeutic targets. Synapse dysfunction and loss are early pathological features of Alzheimer's disease, correlate with cognitive decline and proceed with neuronal death. In the last years, the E3 ubiquitin ligase anaphase promoting complex/cyclosome (APC/C) has emerged as a key regulator of synaptic plasticity and neuronal survival. To this end, the ligase binds Cdh1, its main activator in the brain. However, inactivation of the anaphase promoting complex/cyclosome-Cdh1 complex triggers dendrite disruption, synapse loss and neurodegeneration, leading to memory and learning impairment. Interestingly, oligomerized amyloid-β (Aβ) peptide, which is involved in Alzheimer's disease onset and progression, induces Cdh1 phosphorylation leading to anaphase promoting complex/cyclosome-Cdh1 complex disassembly and inactivation. This causes the aberrant accumulation of several anaphase promoting complex/cyclosome-Cdh1 targets in the damaged areas of Alzheimer's disease brains, including Rock2 and Cyclin B1. Here we review the function of anaphase promoting complex/cyclosome-Cdh1 dysregulation in the pathogenesis of Alzheimer's disease, paying particular attention in the neurotoxicity induced by its molecular targets. Understanding the role of anaphase promoting complex/cyclosome-Cdh1-targeted substrates in Alzheimer's disease may be useful in the development of new effective disease-modifying treatments for this neurological disorder.

Dual Hyper-Graph Regularized Supervised NMF for Selecting Differentially Expressed Genes and Tumor Classification

Non-negative matrix factorization (NMF) is a dimensionality reduction technique based on high-dimensional mapping. It can learn part-based representations effectively. In this paper, we propose a method called Dual Hyper-graph Regularized Supervised Non-negative Matrix Factorization (HSNMF). To encode the geometric information of the data, the hyper-graph is introduced into the model as a regularization term. The advantage of hyper-graph learning is to find higher order data relationship to enhance data relevance. This method constructs the data hyper-graph and the feature hyper-graph to find the data manifold and the feature manifold simultaneously. The application of hyper-graph theory in cancer datasets can effectively find pathogenic genes. The discrimination information is further introduced into the objective function to obtain more information about the data. Supervised learning with label information greatly improves the classification effect. Furthermore, the real datasets of cancer usually contain sparse noise, so the L_2,1-norm is applied to enhance the robustness of HSNMF algorithm. Experiments under The Cancer Genome Atlas (TCGA) datasets verify the feasibility of the HSNMF method.

The role of Anaphase Promoting Complex activation, inhibition and substrates in cancer development and progression

The Anaphase Promoting Complex (APC), a multi-subunit ubiquitin ligase, facilitates mitotic and G1 progression, and is now recognized to play a role in maintaining genomic stability. Many APC substrates have been observed overexpressed in multiple cancer types, such as CDC20, the Aurora A and B kinases, and Forkhead box M1 (FOXM1), suggesting APC activity is important for cell health. We performed BioGRID analyses of the APC coactivators CDC20 and CDH1, which revealed that at least 69 proteins serve as APC substrates, with 60 of them identified as playing a role in tumor promotion and 9 involved in tumor suppression. While these substrates and their association with malignancies have been studied in isolation, the possibility exists that generalized APC dysfunction could result in the inappropriate stabilization of multiple APC targets, thereby changing tumor behavior and treatment responsiveness. It is also possible that the APC itself plays a crucial role in tumorigenesis through its regulation of mitotic progression. In this review the connections between APC activity and dysregulation will be discussed with regards to cell cycle dysfunction and chromosome instability in cancer, along with the individual roles that the accumulation of various APC substrates may play in cancer progression.

Hyper-Graph Regularized Constrained NMF for Selecting Differentially Expressed Genes and Tumor Classification

Non-negative Matrix Factorization (NMF) is a dimensionality reduction approach for learning a parts-based and linear representation of non-negative data. It has attracted more attention because of that. In practice, NMF not only neglects the manifold structure of data samples, but also overlooks the priori label information of different classes. In this paper, a novel matrix decomposition method called Hyper-graph regularized Constrained Non-negative Matrix Factorization (HCNMF) is proposed for selecting differentially expressed genes and tumor sample classification. The advantage of hyper-graph learning is to capture local spatial information in high dimensional data. This method incorporates a hyper-graph regularization constraint to consider the higher order data sample relationships. The application of hyper-graph theory can effectively find pathogenic genes in cancer datasets. Besides, the label information is further incorporated in the objective function to improve the discriminative ability of the decomposition matrix. Supervised learning with label information greatly improves the classification effect. We also provide the iterative update rules and convergence proofs for the optimization problems of HCNMF. Experiments under The Cancer Genome Atlas (TCGA) datasets confirm the superiority of HCNMF algorithm compared with other representative algorithms through a set of evaluations.

Sparse Graph Regularization Non-Negative Matrix Factorization Based on Huber Loss Model for Cancer Data Analysis

Non-negative matrix factorization (NMF) is a matrix decomposition method based on the square loss function. To exploit cancer information, cancer gene expression data often uses the NMF method to reduce dimensionality. Gene expression data usually have some noise and outliers, while the original NMF loss function is very sensitive to non-Gaussian noise. To improve the robustness and clustering performance of the algorithm, we propose a sparse graph regularization NMF based on Huber loss model for cancer data analysis (Huber-SGNMF). Huber loss is a function between L₁-norm and L₂-norm that can effectively handle non-Gaussian noise and outliers. Taking into account the sparsity matrix and data geometry information, sparse penalty and graph regularization terms are introduced into the model to enhance matrix sparsity and capture data manifold structure. Before the experiment, we first analyzed the robustness of Huber-SGNMF and other models. Experiments on The Cancer Genome Atlas (TCGA) data have shown that Huber-SGNMF performs better than other most advanced methods in sample clustering and differentially expressed gene selection.

Does APC/CCDH1 control the human brain size?: An Editorial Highlight for 'A novel human Cdh1 mutation impairs anaphase-promoting complex/cyclosome (APC/C) activity resulting in microcephaly, psychomotor retardation, and epilepsy' on page 103

This editorial highlights a study by Rodriguez, Sanchez-Moran et al. (2019) in the current issue of the Journal of Neurochemistry, in which the authors describe a microcephalic boy carrying the novel heterozygous de novo missense mutation c.560A> G; p.Asp187Gly in Cdh1/Fzr1 encoding the APC/C E3-ubiquitin ligase cofactor CDH1. A functional characterization of mutant APC/C^CDH1 confirms an aberrant division of neural progenitor cells, a condition known to determine the mouse brain cortex size. These data suggest that APC/C^CDH1 may contribute to the regulation of the human brain size.

A novel human Cdh1 mutation impairs anaphase promoting complex/cyclosome activity resulting in microcephaly, psychomotor retardation, and epilepsy

The Fizzy-related protein 1 (Fzr1) gene encodes Cdh1 protein, a coactivator of the E3 ubiquitin ligase anaphase-promoting complex/cyclosome (APC/C). Previously, we found that genetic ablation of Fzr1 promotes the death of neural progenitor cells leading to neurogenesis impairment and microcephaly in mouse. To ascertain the possible translation of these findings in humans, we searched for mutations in the Fzr1 gene in 390 whole exomes sequenced in trio in individuals showing neurodevelopmental disorders compatible with a genetic origin. We found a novel missense (p.Asp187Gly) Fzr1 gene mutation (c.560A>G) in a heterozygous state in a 4-year-old boy, born from non-consanguineous Spanish parents, who presents with severe antenatal microcephaly, psychomotor retardation, and refractory epilepsy. Cdh1 protein levels in leucocytes isolated from the patient were significantly lower than those found in his parents. Expression of the Asp187Gly mutant form of Cdh1 in human embryonic kidney 293T cells produced less Cdh1 protein and APC/C activity, resulting in altered cell cycle distribution when compared with cells expressing wild-type Cdh1. Furthermore, ectopic expression of the Asp187Gly mutant form of Cdh1 in cortical progenitor cells in primary culture failed to abolish the enlargement of the replicative phase caused by knockout of endogenous Cdh1. These results indicate that the loss of function of APC/C-Cdh1 caused by Cdh1 Asp187Gly mutation is a new cause of prenatal microcephaly, psychomotor retardation, and severe epilepsy. Read the Editorial Highlight for this article on page 8. Cover Image for this issue: doi: 10.1111/jnc.14524.