Dataset Information

Genome screen to detect linkage to common susceptibility genes for intracranial and aortic aneurysms.

ABSTRACT:

Background and purpose

Risk for both intracranial aneurysms (IAs) and aortic aneurysms (AAs) is thought to be heritable with mounting evidence for genetic predisposition. The concept of shared risk for these conditions is challenged by differences in age of diagnosis and demographic characteristics. We performed a genomewide linkage analysis in multiplex families with both IA and AA from the Familial Intracranial Aneurysm study.

Methods

Available medical records of subjects who reported IA or abdominal/thoracic AA were reviewed with adjudication as definite/probable, possible, or not a case. To identify genes contributing to the susceptibility for IA and AA, genomewide linkage analysis was performed in the 26 multiplex IA families who had members who also had thoracic or abdominal AA. Individuals (n=91) were defined as affected if they had an IA (definite/probable) or an aortic or thoracic AA (definite/probable).

Results

Maximum logarithm of odds (LOD) scores were found on chromosomes 11 (144 cM; LOD=3.0) and 6 (33 cM; LOD=2.3). In both chromosomal regions, analyses of these same 26 families considering only IA as the disease phenotype produced LOD scores of 1.8 and 1.6, respectively.

Conclusions

Our linkage analysis in these 26 families using the broadest disease phenotype, including IA, abdominal AA, and thoracic AA, supports the concept of shared genetic risk. The chromosome 11 locus appears to confirm earlier independent associations in IA and AA. The chromosome 6 finding is novel. Both warrant further investigation.

SUBMITTER: Worrall BB

PROVIDER: S-EPMC2629798 | biostudies-literature | 2009 Jan

REPOSITORIES: biostudies-literature

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Publications

Genome screen to detect linkage to common susceptibility genes for intracranial and aortic aneurysms.

Worrall Bradford B BB Foroud Tatiana T Brown Robert D RD Connolly E Sander ES Hornung Richard W RW Huston John J Kleindorfer Dawn D Koller Daniel L DL Lai Dongbing D Moomaw Charles J CJ Sauerbeck Laura L Woo Daniel D Broderick Joseph P JP

Stroke 20081023 1

<h4>Background and purpose</h4>Risk for both intracranial aneurysms (IAs) and aortic aneurysms (AAs) is thought to be heritable with mounting evidence for genetic predisposition. The concept of shared risk for these conditions is challenged by differences in age of diagnosis and demographic characteristics. We performed a genomewide linkage analysis in multiplex families with both IA and AA from the Familial Intracranial Aneurysm study.<h4>Methods</h4>Available medical records of subjects who re ...[more]

PMID: 18948608

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Project description:Intracranial aneurysm (IA) is a cerebrovascular disease with a high mortality rate. The pathogenesis of IA remains unclear and the treatment limited. The purpose of the present study was to identify the key genes expressed in IAs and provide the basis for further research and treatment. The raw dataset GSE75436 was downloaded from Gene Expression Omnibus, including 15 IA samples and 15 matched superficial temporal artery (STA) samples. Then, differentially expressed genes (DEGs) were identified using the limma package in R software. Hierarchical clustering analysis was performed on the DEGs using the gplot2 package in R. Database for Annotation, Visualization, and Integrated Discovery (DAVID) online tools were used to perform gene ontology (GO) functional enrichment analysis. DAVID and gene set enrichment analysis were separately used to perform the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. The intersections of the two results were selected as common KEGG pathways. Protein‑protein interaction (PPI) analysis among the DEGs involved in the common KEGG pathways was performed using Search Tool for the Retrieval of Interacting Genes online tools, and visualized with Cytoscape software. A total of 782 DEGs were identified, comprising 392 upregulated and 390 downregulated DEGs. Hierarchical clustering demonstrated that the DEGs could precisely distinguish the IAs from the STAs. The GO enrichment analysis demonstrated that the upregulated DEGs were mainly involved in the inflammatory response and the management of extracellular matrix, and the downregulated DEGs were mainly involved in the process of vascular smooth muscle contraction. The KEGG pathway enrichment analysis demonstrated that the common pathways were 'leishmaniasis', 'Toll‑like receptor signaling pathway' and 'vascular smooth muscle contraction'. In the PPI network, tumor necrosis factor (TNF), interleukin 8 and Toll‑like receptor 4 had the highest degrees; they were associated with the inflammatory response. The Toll‑like receptor signaling pathway and TNF gene may serve as targets for future research and treatment.

Project description:BackgroundIntracranial aneurysms (IAs) are dangerous because of their potential to rupture and cause deadly subarachnoid hemorrhages. Previously, we found significant RNA expression differences in circulating neutrophils between patients with unruptured IAs and aneurysm-free controls. Searching for circulating biomarkers for unruptured IAs, we tested the feasibility of developing classification algorithms that use neutrophil RNA expression levels from blood samples to predict the presence of an IA.MethodsNeutrophil RNA extracted from blood samples from 40 patients (20 with angiography-confirmed unruptured IA, 20 angiography-confirmed IA-free controls) was subjected to next-generation RNA sequencing to obtain neutrophil transcriptomes. In a randomly-selected training cohort of 30 of the 40 samples (15 with IA, 15 controls), we performed differential expression analysis. Significantly differentially expressed transcripts (false discovery rate < 0.05, fold change ≥ 1.5) were used to construct prediction models for IA using four well-known supervised machine-learning approaches (diagonal linear discriminant analysis, cosine nearest neighbors, nearest shrunken centroids, and support vector machines). These models were tested in a testing cohort of the remaining 10 neutrophil samples from the 40 patients (5 with IA, 5 controls), and model performance was assessed by receiver-operating-characteristic (ROC) curves. Real-time quantitative polymerase chain reaction (PCR) was used to corroborate expression differences of a subset of model transcripts in neutrophil samples from a new, separate validation cohort of 10 patients (5 with IA, 5 controls).ResultsThe training cohort yielded 26 highly significantly differentially expressed neutrophil transcripts. Models using these transcripts identified IA patients in the testing cohort with accuracy ranging from 0.60 to 0.90. The best performing model was the diagonal linear discriminant analysis classifier (area under the ROC curve = 0.80 and accuracy = 0.90). Six of seven differentially expressed genes we tested were confirmed by quantitative PCR using isolated neutrophils from the separate validation cohort.ConclusionsOur findings demonstrate the potential of machine-learning methods to classify IA cases and create predictive models for unruptured IAs using circulating neutrophil transcriptome data. Future studies are needed to replicate these findings in larger cohorts.

Project description:BackgroundIntracranial aneurysms are pathological dilatations of the cerebral artery, while rupture of intracranial aneurysms causes life-threatening subarachnoid hemorrhage. The molecular mechanisms of pathogenesis of intracranial aneurysms are poorly understood. MicroRNAs have fundamental roles in modulating vascular biology and disease. In the present study, we carried out a genome-wide characterization on expressions of microRNAs, and performed integrative analyses in conjunction with changes of the transcriptome in human intracranial aneurysms.MethodsGenome-wide microRNA screening was performed in 6 intracranial aneurysmal samples and 6 normal superficial temporal arteries. Each case and control pair was individually matched with gender, age (±5 years), and high blood pressure history. Microarray analysis was performed using Agilent Human miRNA arrays.ResultsAs compared to normal arteries, we identified 157 microRNAs that were differentially expressed in the aneurysmal tissue (P < 0.05 and fold change ≥ 2), including 72 upregulated and 85 downregulated. The changed microRNAs included endothelium-enriched microRNAs such as members of the let-7 family, miR-17, miR-23b, miR-126, hsa-miR-24-1 and miR-222, and vascular smooth muscle-enriched miRNAs such as miR-143 and miR-145. Moreover, miR-1, miR-10a, miR-125b, and miR-26a, which were implicated in modulating vascular smooth muscle cell functions such as proliferation, apoptosis and shift of phenotype, were also changed. In contrast, microRNAs involved in monocyte and macrophage functions, such as miR-155, miR-146a, miR-223, and miR-124a, were not significantly changed. Bioinformatic analysis revealed that the changed microRNAs were associated with several biological processes related to aneurysm formation, including inflammation, dysregulation of extracellular matrix, smooth muscle cell proliferation, programmed cell death, and response to oxidative stress. Interestingly, we found that a subset of the potential microRNA target genes belonged to the protein translation machinery, including various eukaryotic translation initiation factors and ribosomal proteins, and this finding was highly correlated with our previous transcriptome data showing that multiple genes of the ribosomal proteins and translation initiation and elongation factors were significantly downregulated in human intracranial aneurysms.ConclusionsOur results support that dysregulated microRNAs may have a pathogenic role in intracranial aneurysms. Disruption of the protein translation process may have a pathogenic role in the development of intracranial aneurysms.

Dataset Information

Genome screen to detect linkage to common susceptibility genes for intracranial and aortic aneurysms.

Background and purpose

Methods

Results

Conclusions

Publications

Genome screen to detect linkage to common susceptibility genes for intracranial and aortic aneurysms.

Similar Datasets

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