Genetic Insight into Birt-Hogg-Dubé syndrome in Indian patients reveals novel mutations in FLCN

Background: Birt-Hogg-Dub&eacute syndrome (BHDS) is a rare monogenic condition mostly associated with germline mutations at FLCN. It is characterized by either one or more manifestations of primary spontaneous pneumothorax (PSP), skin fibrofolliculomas and renal carcinoma. Here, we comprehensively studied germline mutations in BHDS patients and asymptomatic members from 15 Indian families. Methods: Targeted amplicon NGS and Sanger sequencing was performed to detect germline mutations at FLCN in 31 clinically diagnosed patients and 74 asymptomatic family members. Functional effects and protein-protein interaction of FLCN variants were evaluated in-silico and molecular docking method. Family-based association study between pathogenic mutations and BHDS was also performed. Germline mutations at genes associated with phenotypically similar diseases were also addressed in few families. Results: Six different types of pathogenic FLCN mutations were observed in the patients. Two of them: 11-nucleotide deletion (c.1150_1160delGTCCAGTCAGC) and splice acceptor mutation (c.1301-1G>A), were novel mutations. Two unreported Clinvar pathogenic mutations: stop-gain (c.634C>T) and 4-nucleotide duplication (c.1329_1332dupAGCC), and known mutations: hotspot mutation (c.1285delC) and splice donor mutations (c.1300+1G>A) were also detected. All these mutations greatly affected the protein stability and FLCN-FNIP2 protein interaction. Family-based association studies suggested pathogenic FLCN mutations are significantly associated with BHDS. Two pathogenic SNPs, rs1801133 and rs138189536, at MTHFR, associated with Homocystinuria, were found in one family. Conclusion: Pathogenic mutations at FLCN may play key roles in deregulating metabolic pathways leading to disease pathogenesis. Instead of FLCN mutations, MTHFR pathogenic SNPs were also detected in clinically diagnosed BHDS patients, therefore, genetic evaluation is necessary to avoid confounding diagnosis.

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Discovering Interaction Motifs from Protein Interaction Networks

Biological Data Mining in Protein Interaction Networks ◽

10.4018/978-1-60566-398-2.ch007 ◽

2009 ◽

pp. 99-116 ◽

Cited By ~ 1

Author(s):

Hugo Willy

Keyword(s):

Protein Interaction ◽

Protein Interactions ◽

Protein Interaction Networks ◽

Interaction Networks ◽

Protein Interaction Data ◽

Interaction Data ◽

Protein Patterns ◽

Protein Protein Interaction ◽

Family Based ◽

High Throughput Experiments

Recent breakthroughs in high throughput experiments to determine protein-protein interaction have generated a vast amount of protein interaction data. However, most of the experiments could only answer the question of whether two proteins interact but not the question on the mechanisms by which proteins interact. Such understanding is crucial for understanding the protein interaction of an organism as a whole (the interactome) and even predicting novel protein interactions. Protein interaction usually occurs at some specific sites on the proteins and, given their importance, they are usually well conserved throughout the evolution of the proteins of the same family. Based on this observation, a number of works on finding protein patterns/motifs conserved in interacting proteins have emerged in the last few years. Such motifs are collectively termed as the interaction motifs. This chapter provides a review on the different approaches on finding interaction motifs with a discussion on their implications, potentials and possible areas of improvements in the future.

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A Pathway and Network Oriented Approach to Enlighten Molecular Mechanisms of Type 2 Diabetes Using Multiple Association Studies

10.1101/2020.01.14.905547 ◽

2020 ◽

Author(s):

Burcu Bakir-Gungor ◽

Miray Unlu Yazici ◽

Gokhan Goy ◽

Mustafa Temiz

Keyword(s):

Diabetes Mellitus ◽

Type 2 Diabetes ◽

Protein Interaction ◽

Molecular Mechanisms ◽

Association Studies ◽

Genome Wide Association Studies ◽

Protein Protein Interaction ◽

Ppi Networks ◽

Oriented Approach

AbstractDiabetes Mellitus (DM) is a group of metabolic disorder that is characterized by pancreatic dysfunction in insulin producing beta cells, glucagon secreting alpha cells, and insulin resistance or insulin in-functionality related hyperglycemia. Type 2 Diabetes Mellitus (T2D), which constitutes 90% of the diabetes cases, is a complex multifactorial disease. In the last decade, genome-wide association studies (GWASs) for type 2 diabetes (T2D) successfully pinpointed the genetic variants (typically single nucleotide polymorphisms, SNPs) that associate with disease risk. However, traditional GWASs focus on the ‘the tip of the iceberg’ SNPs, and the SNPs with mild effects are discarded. In order to diminish the burden of multiple testing in GWAS, researchers attempted to evaluate the collective effects of interesting variants. In this regard, pathway-based analyses of GWAS became popular to discover novel multi-genic functional associations. Still, to reveal the unaccounted 85 to 90% of T2D variation, which lies hidden in GWAS datasets, new post-GWAS strategies need to be developed. In this respect, here we reanalyze three meta-analysis data of GWAS in T2D, using the methodology that we have developed to identify disease-associated pathways by combining nominally significant evidence of genetic association with the known biochemical pathways, protein-protein interaction (PPI) networks, and the functional information of selected SNPs. In this research effort, to enlighten the molecular mechanisms underlying T2D development and progress, we integrated different in-silico approaches that proceed in top-down manner and bottom-up manner, and hence presented a comprehensive analysis at protein subnetwork, pathway, and pathway subnetwork levels. Our network and pathway-oriented approach is based on both the significance level of an affected pathway and its topological relationship with its neighbor pathways. Using the mutual information based on the shared genes, the identified protein subnetworks and the affected pathways of each dataset were compared. While, most of the identified pathways recapitulate the pathophysiology of T2D, our results show that incorporating SNP functional properties, protein-protein interaction networks into GWAS can dissect leading molecular pathways, which cannot be picked up using traditional analyses. We hope to bridge the knowledge gap from sequence to consequence.

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