Genetic variants of glutamate receptor gene family in Taiwanese Kawasaki disease children with coronary artery aneurysms

GRIK1 promotes glioblastoma malignancy and is a novel prognostic factor of poor prognosis

Primary tumors of the central nervous system (CNS) are classified into over 100 different histological types. The most common type of glioma is derived from astrocytes, and the most invasive glioblastoma (WHO IV) accounts for over 57% of these tumors. Glioblastoma (GBM) is the most common and fatal tumor of the CNS, with strong growth and invasion capabilities, which makes complete surgical resection almost impossible. Despite various treatment methods such as surgery, radiotherapy, and chemotherapy, glioma is still an incurable disease, and the median survival time of patients with GBM is shorter than 15 months. Thus, molecular mechanisms of GBM characteristic invasive growth need to be clarified to improve the poor prognosis. Glutamate ionotropic receptor kainate type subunit 1 (GRIK1) is essential for brain function and is involved in many mental and neurological diseases. However, GRIK1's pathogenic roles and mechanisms in GBM are still unknown. Single-nuclear RNA sequencing of primary and recurrent GBM samples revealed that GRIK1 expression was noticeably higher in the recurrent samples. Moreover, immunohistochemical staining of an array of GBM samples showed that high levels of GRIK1 correlated with poor prognosis of GBM, consistent with The Cancer Genome Atlas database. Knockdown of GRIK1 retarded GBM cells growth, migration, and invasion. Taken together, these findings show that GRIK1 is a unique and important component in the development of GBM and may be considered as a biomarker for the diagnosis and therapy in individuals with GBM.

Classification models using circulating neutrophil transcripts can detect unruptured intracranial aneurysm

Background

Intracranial aneurysms (IAs) are dangerous because of their potential to rupture. We previously found significant RNA expression differences in circulating neutrophils between patients with and without unruptured IAs and trained machine learning models to predict presence of IA using 40 neutrophil transcriptomes. Here, we aim to develop a predictive model for unruptured IA using neutrophil transcriptomes from a larger population and more robust machine learning methods.

Methods

Neutrophil RNA extracted from the blood of 134 patients (55 with IA, 79 IA-free controls) was subjected to next-generation RNA sequencing. In a randomly-selected training cohort (n = 94), the Least Absolute Shrinkage and Selection Operator (LASSO) selected transcripts, from which we constructed prediction models via 4 well-established supervised machine-learning algorithms (K-Nearest Neighbors, Random Forest, and Support Vector Machines with Gaussian and cubic kernels). We tested the models in the remaining samples (n = 40) and assessed model performance by receiver-operating-characteristic (ROC) curves. Real-time quantitative polymerase chain reaction (RT-qPCR) of 9 IA-associated genes was used to verify gene expression in a subset of 49 neutrophil RNA samples. We also examined the potential influence of demographics and comorbidities on model prediction.

Results

Feature selection using LASSO in the training cohort identified 37 IA-associated transcripts. Models trained using these transcripts had a maximum accuracy of 90% in the testing cohort. The testing performance across all methods had an average area under ROC curve (AUC) = 0.97, an improvement over our previous models. The Random Forest model performed best across both training and testing cohorts. RT-qPCR confirmed expression differences in 7 of 9 genes tested. Gene ontology and IPA network analyses performed on the 37 model genes reflected dysregulated inflammation, cell signaling, and apoptosis processes. In our data, demographics and comorbidities did not affect model performance.

Conclusions

We improved upon our previous IA prediction models based on circulating neutrophil transcriptomes by increasing sample size and by implementing LASSO and more robust machine learning methods. Future studies are needed to validate these models in larger cohorts and further investigate effect of covariates.

TCF4 and GRM8 gene polymorphisms and risk of schizophrenia in an Iranian population: a case-control study

TCF4 and GRM8, two significant genes involved in the normal nervous development and glutamate pathway, are thought to be involved in the pathogenesis of schizophrenia (SCZ). We aimed to explore the association of TCF4 and GRM8 gene polymorphisms with risk of SCZ. The rs8766 in TCF4 and rs712723 in GRM8 were selected for genotyping in a set of Iranian case-control samples including 215 patients and 220 matched healthy controls using polymerase chain reaction-restriction fragment length polymorphism. Although rs8766 increased the OR, we found that rs8766 allele and genotype frequencies were not significantly different between case and control groups and a significant association cannot be suggested for the selected SNP. However, allele C and genotype CC (allele C: OR 1.48, 95% CI 1.13-1.94; genotype CC: OR 1.71, 95% CI 1.09-2.68) of rs712723 polymorphism was found to have a significant association with risk of SCZ. Frequency of allele C (P = 0.003) and genotype CC (P = 0.017) was higher in the schizophrenic patients, while allele T (P = 0.003) and genotype TT (P = 0.028) frequencies were found lower in patients. Our findings indicate that rs712723 in GRM8 may play an important role in the pathogenesis of SCZ. However, our conclusion needs to be confirmed in other population.

Association of PECAM-1 Gene Polymorphisms with Kawasaki Disease in Chinese Children

Kawasaki disease (KD) is an acute systemic vasculitis complicated by development of coronary artery lesions. PECAM-1 is a kind of cell adhesion molecule, which plays an important role in coronary artery disease. The relationship between PECAM-1 gene polymorphisms and their susceptibility to Kawasaki diseases (KD) is still unclear. In our study, we examined the PECAM-1 gene polymorphisms in 44 KD patients and 59 healthy children and revealed the correlation of PECAM-1 gene polymorphisms in KD children with and without coronary artery lesions (CAL).

Genome-Wide Association Study Identifies Novel Susceptibility Genes Associated with Coronary Artery Aneurysm Formation in Kawasaki Disease

Kawasaki disease (KD) or Kawasaki syndrome is known as a vasculitis of small to medium-sized vessels, and coronary arteries are predominantly involved in childhood. Generally, 20-25% of untreated with IVIG and 3-5% of treated KD patients have been developed coronary artery lesions (CALs), such as dilatation and aneurysm. Understanding how coronary artery aneurysms (CAAs) are established and maintained in KD patients is therefore of great importance. Upon our previous genotyping data of 157 valid KD subjects, a genome-wide association study (GWAS) has been conducted among 11 (7%) CAA-developed KD patients to reveal five significant genetic variants passed pre-defined thresholds and resulted in two novel susceptibility protein-coding genes, which are NEBL (rs16921209 (P = 7.44 × 10(-9); OR = 32.22) and rs7922552 (P = 8.43 × 10(-9); OR = 32.0)) and TUBA3C (rs17076896 (P = 8.04 × 10(-9); OR = 21.03)). Their known functions have been reported to associate with cardiac muscle and tubulin, respectively. As a result, this might imply their putative roles of establishing CAAs during KD progression. Additionally, various model analyses have been utilized to determine dominant and recessive inheritance patterns of identified susceptibility mutations. Finally, all susceptibility genes hit by significant genetic variants were further investigated and the top three representative gene-ontology (GO) clusters were regulation of cell projection organization, neuron recognition, and peptidyl-threonine phosphorylation. Our results help to depict the potential routes of the pathogenesis of CAAs in KD patients and will facilitate researchers to improve the diagnosis and prognosis of KD in personalized medicine.