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In summary, we identified the second ADCA family with the heterozygous mutation (NM001510.4: c.1966C>G, p.Leu656Val) in the GRID2 gene; this variant was found in an Algerian family using whole-exome analysis. We should screen for GRID2 variants in the case of families with pure cerebellar ataxia in ADCA. Further studies are required to elucidate the genotype-phenotype correlation in GRID2-related ataxias.




Related articles: "grid2"


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In some reported large consanguineous females, females with the same mutations often tend to have milder symptoms, as compared with related males.15222324This gender imbalance might be due to modifier genes on the X chromosome but needs further exploration to be confirmed. Also, the majority of cases had mild to moderate cognitive impairment and even a few cases had early-onset dementia. Similar impairment of cognition also has been reported in GRID2 knockout hot foot mice. These findings reinforce the putative implications regarding the role of the cerebellum in controlling cognitive functions, but the potential role of theGRID2gene in controlling the function of the cerebral cortex cannot be ruled out altogether.


Glutamate receptor ionotropic, delta-2 (GRID2), also known as GluR delta-2 subunit (GluD2), is a protein that is encoded by the GRID2 gene. The protein belongs to the family of ionotropic glutamate receptors. It is a multi-pass membrane protein which is expressed selectively in cerebellar Purkinje cells. Ionotropic glutamate receptors are the major excitatory neurotransmitter receptors in the mammalian brain. Point mutations in mouse ortholog are related to the phenotype known as 'lurcher'. Mutations are in the heterozygous state, resulting in ataxia caused by selective, cell-autonomous apoptosis of cerebellar Purkinje cells during postnatal development. This mutant homozygous mouse dies shortly after birth from massive loss of mid- and hindbrain neurons during late embryogenesis. In addition, this protein is essential in synapse organization between Purkinje cells and parallel fibers. Alternate splicing leads to multiple transcript variants encoding distinct isoforms. Mutations in this gene cause cerebellar ataxia in humans.


Several diseases are associated with GRID2, such as spinocerebellar ataxia, spinocerebellar ataxia and autosomal recessive. Among its related pathways are long-term depression and peptide ligand-binding receptors. Deletions involving the GRID2 gene, encoding the glutamate receptor subunit delta-2 protein, have been reported in families segregating autosomal recessive cerebellar syndrome with infantile onset. Affected individuals exhibit abnormal eye movements, developmental delay, slowly progressive hypotonia, ataxia, and dysarthria. Radiological investigation revealed cerebellar atrophy, in a small number of patients with pontine involvement. In addition, heterozygous GRID2 mutations have been identified, including three missense variants, ocular symptoms, cerebellar ataxia, and cognitive impairment.


The article reveals that a wide range of supratentorial brain abnormalities, increased peripheral muscle tone, loss of oculomotor symptoms, and new missense mutation increase the genetic and clinical variability in GRID2 related cerebellar syndrome.


From AD donors, samples with only amyloid-β (OC) or both amyloid-β and tau pathology (OTC) were analyzed (Fig. 4a). To determine whether different microglia subtypes were associated with the degree of pathology, the level of amyloid-β and tau was quantified and correlated to the percentage of microglia in each subcluster (Fig. 4b, Fig. S6d, Fig. S7a). Strong positive correlations were observed between amyloid-β load and AD1-microglia abundance in samples that contained only amyloid-β pathology (Fig. 4b) but not in the samples that contained both amyloid-β and tau pathology (Fig. 4b, S7c). This indicates that AD1-microglia are associated with amyloid-β, but when tau pathology is present this correlation is absent. This suggests that additional presence of tau induces an additional microglia subtype. In samples that contained both amyloid-β and tau pathology (AD-OTC), significant positive correlations were detected between tau-load and AD2-microglia abundance (Fig. 4b, Fig. S7c). Additionally, negative correlations were identified between homeostasis clusters and amyloid-β and/or tau-load in both regions (Fig. 4b, Fig. S7c), suggesting a decrease in homeostatic microglia abundance in the presence of pathology. AD1 microglia abundance did not correlate with amyloid-β load in the OTC samples. This may be due to the increased abundance of AD2 microglia associated with tau pathology in these samples. As we used relative subcluster abundance as a variable, AD1 and AD2 microglia abundance are two dependent variables, and if AD2 microglia abundance increases in the OTC samples, AD1 abundance will relatively decrease and no longer (positively) correlate with amyloid-β load.


In sporadic AD, genome-wide association studies (GWAS) identified several risk loci and genes located on these loci are expressed in immune-related tissues and cell types [14]. Of the 63 AD-risk genes [7] expressed in human microglia [9], 15 were significantly enriched and highly expressed in AD1-microglia, and six genes were moderately enriched in AD2-microglia (Fig. S7g). This finding is in line with a recent mouse study of Sierksma et al. (2020), where it was shown that the genetic risk of AD is functionally associated with the microglia response to amyloid-β pathology and not to phospho-tau pathology, suggesting that amyloid-β pathology is upstream of tau pathology [30]. This indicates that the immune response of AD1-microglia to amyloid-β pathology is involved in the onset and progression of AD.


FIGURE 1. Clustering analysis of osteosarcoma samples based on mRNA levels of autophagy-related genes. (A) An elbow graph determined the optimal number of clusters. The horizontal axis represented the number of clusters K, and the vertical axis represented the sum of the squared errors (SSE). The point where the decline tended to be gentle was the number of the optimal cluster. (B) Schematic diagram of sample clustering. Different colors represented different clusters. (C) Heat map of the expression of autophagy-related genes in two types of samples. Behavioral genes were listed as samples. Red indicated high expression and green indicated low expression. The age and sex of the sample were marked with different colors above the heat map. (D) PCA analysis. The dots with different colors represented samples in different groups. The closer the dots, the more similar the expression of autophagy-related genes in the samples. (E) Kaplan-Meier curve. The horizontal axis represented time, the vertical axis represented survival rate, and the colors indicated different groupings. The p value was determined based on the log-rank test.


FIGURE 2. The risk score model predicted the survival of patients with osteosarcoma. (A) Forest map of autophagy-related genes significantly related to overall survival in univariate analysis. HR was Hazard ratio, and 95% CI was 95% confidence interval. (B) Diagram of the optimal number of genes in the LASSO regression model. The horizontal axis represented log (lambda), and the vertical axis represented the partial likelihood deviance. The Lambda value corresponding to the minimum value was the best. (C) Coefficient spectrum of LASSO Cox regression model. (D) The risk scores distribution of samples in the TARGET dataset. A point indicated a sample, a red point represented a sample with a higher risk score, a green point indicated a sample with a lower risk score, and the intersecting point represented the optimal risk score. (E) The Cluster heat map of 14 autophagy-related gene expressions in the TARGET dataset. Behavioral genes were listed as samples. Red represented high expression and blue represented low expression. Different colors indicated the sample groups above the heat map. (F) Kaplan-Meier survival curve of samples from TARGET dataset. The horizontal axis represented time, the vertical axis indicated survival rate, and different colors represented different groups. (G) The time-dependent ROC curve of samples from TARGET dataset. The horizontal axis indicated the false positive, the vertical axis represented the true positive, and the accuracy of the prediction was evaluated by AUC value (area under curve). (H) The distribution of risk scores of samples from integrated GEO dataset. (I) Cluster heat map of the expression levels of 14 autophagy-related genes from integrated GEO dataset. (J) Kaplan-Meier survival curve of GEO integrated dataset. (K) The time-dependent ROC curve of integrated GEO dataset.


Background: Osteosarcoma is a common malignancy of bone with inferior survival outcome. Autophagy can exert multifactorial influence on tumorigenesis and tumor progression. However, the specific function of genes related to autophagy in the prognosis of osteosarcoma patients remains unclear. Herein, we aimed to explore the association of genes related to autophagy with the survival outcome of osteosarcoma patients.


Methods: The autophagy-associated genes that were related to the prognosis of osteosarcoma were optimized by LASSO Cox regression analysis. The survival of osteosarcoma patients was forecasted by multivariate Cox regression analysis. The immune infiltration status of 22 immune cell types in osteosarcoma patients with high and low risk scores was compared by using the CIBERSORT tool.


Results: The risk score model constructed according to 14 autophagy-related genes (ATG4A, BAK1, BNIP3, CALCOCO2, CCL2, DAPK1, EGFR, FAS, GRID2, ITGA3, MYC, RAB33B, USP10, and WIPI1) could effectively predict the prognosis of patients with osteosarcoma. A nomogram model was established based on risk score and metastasis. 041b061a72


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