Transcriptomic Changes Resulting From STK32B Overexpression Identify Pathways Potentially Relevant to Essential Tremor

Liao, Calwing; Sarayloo, Faezeh; Vuokila, Veikko; Rochefort, Daniel; Akçimen, Fulya; Diamond, Simone; Houle, Gabrielle; Laporte, Alexandre D.; Spiegelman, Dan; He, Qiaojun; Catoire, Hélène; Rouleau, Guy A.

doi:10.3389/fgene.2020.00813

Cited by 13 publications

(11 citation statements)

References 19 publications

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“…Through gene-set enrichment analysis, we identified axonogenesis as an important cellular process for the disease, consistent with previous studies that implicate the importance of axons. Furthermore, we found significant associations between ET and the cerebellum, providing further evidence that ET may be a cerebellar disorder or reflective of neurons driving the signal due to high proportion of neurons in the cerebellum. …”

Section: Discussionmentioning

confidence: 90%

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Association of Essential Tremor With Novel Risk Loci

et al. 2022

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IMPORTANCE Essential tremor (ET) is one of the most common movement disorders, affecting 5% of the general population older than 65 years. Common variants are thought to contribute toward susceptibility to ET, but no variants have been robustly identified. OBJECTIVE To identify common genetic factors associated with risk of ET. DESIGN, SETTING, AND PARTICIPANTS Case-control genome-wide association study. Inverse-variance meta-analysis was used to combine cohorts. Multicenter samples collected from European populations were collected from January 2010 to September 2019 as part of an ongoing study. Included patients were clinically diagnosed with or reported having ET. Control individuals were not diagnosed with or reported to have ET. Of 485 250 individuals, data for 483 054 passed data quality control and were used. MAIN OUTCOMES AND MEASURES Genotypes of common variants associated with risk of ET. RESULTS Of the 483 054 individuals included, there were 7177 with ET (3693 [51.46%] female; mean [SD] age, 62.66 [15.12] years), and 475 877 control individuals (253 785 [53.33%] female; mean [SD] age, 56.40 [17.6] years). Five independent genome-wide significant loci and were identified and were associated with approximately 18% of ET heritability. Functional analyses found significant enrichment in the cerebellar hemisphere, cerebellum, and axonogenesis pathways. Genetic correlation (r), which measures the degree of genetic overlap, revealed significant common variant overlap with Parkinson disease (r, 0.28; P = 2.38 × 10 −8 ) and depression (r, 0.12; P = 9.78 × 10 −4 ). A separate fine-mapping of transcriptome-wide association hits identified genes such as BACE2, LRRN2, DHRS13, and LINC00323 in disease-relevant brain regions, such as the cerebellum. CONCLUSIONS AND RELEVANCEThe results of this genome-wide association study suggest that a portion of ET heritability can be explained by common genetic variation and can help identify new common genetic risk factors for ET.

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Section: Discussionmentioning

confidence: 90%

“…17, 18 The loci from these GWAS implicated nearby genes, such as STK32B and LINGO1, for which subsequent replication studies were conducted. [19][20][21][22][23] However, most of these GWAS loci had conflicting replication results and were conducted in smaller cohorts.…”

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confidence: 99%

Association of Essential Tremor With Novel Risk Loci

et al. 2022

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“…Stk32b was significantly decreased in both Homo and Het PKCγ-A24E mice. Stk32b encodes the serine/threonine kinase YANK2, which has not been well characterized [ 26 ]. The only DEG significantly upregulated in both Homo and Het PKCγ-A24E mice, Stk17b , belongs to the same family of kinases and encodes the serine/threonine kinase DRAK2.…”

Section: Resultsmentioning

confidence: 99%

Transcriptome Profile of a New Mouse Model of Spinocerebellar Ataxia Type 14 Implies Changes in Cerebellar Development

Mezey

Kapfhammer

Shimobayashi

2022

Genes

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The autosomal dominant inherited spinocerebellar ataxias (SCAs) are a group of neurodegenerative disorders characterized by cerebellar atrophy and loss of Purkinje neurons. Spinocerebellar ataxia type 14 (SCA14) is a rare variant of SCAs caused by missense mutations or deletions in the PRKCG gene encoding the protein kinase C γ (PKCγ). Although mutated PKCγs are responsible for SCA14, it is still unclear exactly how mutated PKCγs are involved in SCA14 pathogenesis. Therefore, it is important to study how PKCγ signaling is altered in the cerebellum, which genes or signaling pathways are affected, and how this leads to neurological disease. In this study, we used a mouse line carrying a knock-in pseudo-substrate domain mutation in PKCγ (PKCγ-A24E) as an SCA14 model and performed RNA sequencing (RNA-seq) analysis at an early developmental timepoint (postnatal day 15) to investigate changes in the gene profile compared to wildtype mice. We analyzed both heterozygous (Het) PKCγ-A24E mice and homozygous (Homo) PKCγ-A24E mice for transcriptomic changes. The Het PKCγ-A24E mice reflects the situation observed in human SCA14 patient, while Homo PKCγ-A24E mice display stronger phenotypes with respect to Purkinje cell development and behavior. Our findings highlight an abundance of modifications affecting genes involved in developmental processes, suggesting that at least a part of the final phenotype is shaped by altered cerebellar development and is not only caused by changes in mature animals.

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“…Indeed, the role of WISP3 seems to be different in different cancers [49], suggesting its complex regulatory mechanisms. STK32B is mainly associated with idiopathic tremor and anxiety [50,51], but Parris et al found that it may be a marker for oral squamous cell carcinoma [52]. AL359853.1, AC110285.3, and GAGE2 have also been noted to be possibly associated with the prognosis of HCC [53][54][55].…”

Section: Discussionmentioning

confidence: 99%

Screening of Hub Genes in Hepatocellular Carcinoma Based on Network Analysis and Machine Learning

Xie

Huang

Zhang

et al. 2022

Computational and Mathematical Methods in Medicine

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Hepatocellular carcinoma (LIHC) is the fifth common cancer worldwide, and it requires effective diagnosis and treatment to prevent aggressive metastasis. The purpose of this study was to construct a machine learning-based diagnostic model for the diagnosis of liver cancer. Using weighted correlation network analysis (WGCNA), univariate analysis, and Lasso-Cox regression analysis, protein-protein interactions network analysis is used to construct gene networks from transcriptome data of hepatocellular carcinoma patients and find hub genes for machine learning. The five models, including gradient boosting, random forest, support vector machine, logistic regression, and integrated learning, were to identify a multigene prediction model of patients. Immunological assessment, TP53 gene mutation and promoter methylation level analysis, and KEGG pathway analysis were performed on these groups. Potential drug molecular targets for the corresponding hepatocellular carcinomas were obtained by molecular docking for analysis, resulting in the screening of 2 modules that may be relevant to the survival of hepatocellular carcinoma patients, and the construction of 5 diagnostic models and multiple interaction networks. The modes of action of drug-molecule interactions that may be effective against hepatocellular carcinoma core genes CCNA2, CCNB1, and CDK1 were investigated. This study is expected to provide research ideas for early diagnosis of hepatocellular carcinoma.

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Transcriptomic Changes Resulting From STK32B Overexpression Identify Pathways Potentially Relevant to Essential Tremor

Cited by 13 publications

References 19 publications

Association of Essential Tremor With Novel Risk Loci

Association of Essential Tremor With Novel Risk Loci

Transcriptome Profile of a New Mouse Model of Spinocerebellar Ataxia Type 14 Implies Changes in Cerebellar Development

Screening of Hub Genes in Hepatocellular Carcinoma Based on Network Analysis and Machine Learning

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