Identification of a de novo LRP1 mutation in a Saudi family with Tetralogy of Fallot.

BACKGROUND: Tetralogy of Fallot (TOF) is a rare, complex congenital heart defect caused by genetic and environmental interactions that results in abnormal heart development during the early stages of pregnancy. Genetic basis of TOF in Saudi populations is not yet studied. Therefore, the objective of this study is to screen for the molecular defects causing TOF in Saudi patients. METHODS: A family with non-syndromic TOF was recruited from the Western region of Saudi Arabia. Whole exome sequencing (WES) was performed on the proband and her parents. The identified candidate variant was verified by sanger sequencing. Also, different computational biology tools were used to figure out how candidate variants affect the structure and function of candidate protein involved in TOF. RESULTS: A novel heterozygous de novo mutation in LRP1 (p. G3311D) gene was identified in the index case. Also, this variant was absent in the in-house exome sequencing data of 80 healthy Saudi individuals. This variant was predicted to be likely pathogenic, as it negatively affects the biophysical chemical properties and stability of the protein. Furthermore, functional biology data from knock out mouse models confirms that molecular defects in LRP1 gene leads to cardiac defects and lethality. This variant was not previously reported in both Arab and global population genetic databases. CONCLUSION: The findings in this study postulate that the LRP1 variant has a role in TOF pathogenesis and facilitate accurate diagnosis as well as the understanding of underlying molecular mechanisms and pathophysiology of the disease.

Integrative weighted molecular network construction from transcriptomics and genome wide association data to identify shared genetic biomarkers for COPD and lung cancer.

Chronic obstructive pulmonary disease (COPD) is a multifactorial progressive airflow obstruction in the lungs, accounting for high morbidity and mortality across the world. This study aims to identify potential COPD blood-based biomarkers by analyzing the dysregulated gene expression patterns in blood and lung tissues with the help of robust computational approaches. The microarray gene expression datasets from blood (136 COPD and 6 controls) and lung tissues (16 COPD and 19 controls) were analyzed to detect shared differentially expressed genes (DEGs). Then these DEGs were used to construct COPD protein network-clusters and functionally enrich them against gene ontology annotation terms. The hub genes in the COPD network clusters were then queried in GWAS catalog and in several cancer expression databases to explore their pathogenic roles in lung cancers. The comparison of blood and lung tissue datasets revealed 63 shared DEGs. Of these DEGs, 12 COPD hub gene-network clusters (SREK1, TMEM67, IRAK2, MECOM, ASB4, C1QTNF2, CDC42BPA, DPF3, DET1, CCDC74B, KHK, and DDX3Y) connected to dysregulations of protein degradation, inflammatory cytokine production, airway remodeling, and immune cell activity were prioritized with the help of protein interactome and functional enrichment analysis. Interestingly, IRAK2 and MECOM hub genes from these COPD network clusters are known for their involvement in different pulmonary diseases. Additional COPD hub genes like SREK1, TMEM67, CDC42BPA, DPF3, and ASB4 were identified as prognostic markers in lung cancer, which is reported in 1% of COPD patients. This study identified 12 gene network- clusters as potential blood based genetic biomarkers for COPD diagnosis and prognosis.