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Mutations in PTEN-induced kinase 1 (PINK1) gene cause PARK6 familial Parkinsonism, and loss of the stability of PINK1 may also contribute to sporadic Parkinson's disease (PD). Degradation of PINK1 occurs predominantly through the ubiquitin proteasome system (UPS), however, to date, few of the proteins have been found to regulate the degradation of PINK1. Using the yeast two-hybrid system and pull-down methods, we identified bcl-2-associated athanogene 5 (BAG5), a BAG family member, directly interacted with PINK1. We showed that BAG5 stabilized PINK1 by decreasing the ubiquitination of PINK1. Interestingly, BAG5 rescued MPP+- and rotenone-induced mitochondria dysfunction by up-regulating PINK1 in vitro. In PINK1-null mice and MPTP-treated mice, BAG5 significantly increased in the substantia nigra pars compacta (SNpc) although PINK1 was decreased. Our findings indicated that BAG5, as a key protein to stabilize PINK1, is a promising therapeutic tool for preventing mitochondrial dysfunction following oxidative stress. [ABSTRACT FROM AUTHOR]

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition whose current psychiatric diagnostic process is subjective and behavior-based. In contrast, functional magnetic resonance imaging (fMRI) can objectively measure brain activity and is useful for identifying brain disorders. However, the ASD diagnostic models employed to date have not reached satisfactory levels of accuracy. This study proposes the use of MAACNN, a method that utilizes multi-view convolutional neural networks (CNNs) in conjunction with attention mechanisms for identifying ASD in multi-scale fMRI. The proposed algorithm effectively combines unsupervised and supervised learning. In the initial stage, we employ stacked denoising autoencoders, an unsupervised learning method for feature extraction, which provides different nodes to adapt to multi-scale data. In the subsequent stage, we perform supervised learning by employing multi-view CNNs for classification and obtain the final results. Finally, multi-scale data fusion is achieved by using the attention fusion mechanism. The ABIDE dataset is used to evaluate the model we proposed., and the experimental results show that MAACNN achieves superior performance with 75.12% accuracy and 0.79 AUC on ABIDE-I, and 72.88% accuracy and 0.76 AUC on ABIDE-II. The proposed method significantly contributes to the clinical diagnosis of ASD. [ABSTRACT FROM AUTHOR]

Autosomal recessive cerebellar ataxia (ARCA) comprises a large and heterogeneous group of neurodegenerative disorders. For many affected patients, the genetic cause remains undetermined. Through whole-exome sequencing, we identified compound heterozygous mutations in ubiquitin-like modifier activating enzyme 5 gene (UBA5) in two Chinese siblings presenting with ARCA. Moreover, copy number variations in UBA5 or ubiquitin-fold modifier 1 gene (UFM1) were documented with the phenotypes of global developmental delays and gait disturbances in the ClinVar database. UBA5 encodes UBA5, the ubiquitin-activating enzyme of UFM1. However, a crucial role for UBA5 in human neurological disease remains to be reported. Our molecular study of UBA5-R246X revealed a dramatically decreased half-life and loss of UFM1 activation due to the absence of the catalytic cysteine Cys250. UBA5-K310E maintained its interaction with UFM1, although with less stability, which may affect the ability of this UBA5 mutant to activate UFM1. Drosophila modeling revealed that UBA5 knockdown induced locomotive defects and a shortened lifespan accompanied by aberrant neuromuscular junctions (NMJs). Strikingly, we found that UFM1 and E2 cofactor knockdown induced markedly similar phenotypes. Wild-type UBA5, but not mutant UBA5, significantly restored neural lesions caused by the absence of UBA5. The finding of a UBA5 mutation in cerebellar ataxia suggests that impairment of the UFM1 pathway may contribute to the neurological phenotypes of ARCA. [ABSTRACT FROM AUTHOR]

Autosomal recessive cerebellar ataxias are a group of neurodegenerative disorders that are characterized by complex clinical and genetic heterogeneity. Although more than 20 disease-causing genes have been identified, many patients are still currently without a molecular diagnosis. In a two-generation autosomal recessive cerebellar ataxia family, we mapped a linkage to a minimal candidate region on chromosome 16p13.3 flanked by single-nucleotide polymorphism markers rs11248850 and rs1218762. By combining the defined linkage region with the whole-exome sequencing results, we identified a homozygous mutation (c.493CT) in CHIP (NM_005861) in this family. Using Sanger sequencing, we also identified two compound heterozygous mutations (c.389AT/c.441GT; c.621C>G/c.707GC) in CHIP gene in two additional kindreds. These mutations co-segregated exactly with the disease in these families and were not observed in 500 control subjects with matched ancestry. CHIP colocalized with NR2A, a subunit of the N-methyl-D-aspartate receptor, in the cerebellum, pons, medulla oblongata, hippocampus and cerebral cortex. Wild-type, but not disease-associated mutant CHIPs promoted the degradation of NR2A, which may underlie the pathogenesis of ataxia. In conclusion, using a combination of whole-exome sequencing and linkage analysis, we identified CHIP, encoding a U-box containing ubiquitin E3 ligase, as a novel causative gene for autosomal recessive cerebellar ataxia. [ABSTRACT FROM AUTHOR]