Rastgele Diyabet SNP Olayları ve Moleküler Etkileşim Komşuluğu

Year 2019, Volume: 3 Issue: 2, 99 - 105, 31.08.2019

Ertuğrul Dalgıç

Abstract

Amaç: Genomik çalışmalar, tip 2 diabetes mellitus (T2DM) dahil olmak üzere, birçok hastalık ve diğer fenotiplerle önemli ilişkileri olanTek Nükleotid Polimorfizmleri koleksiyonu sunmaktadır. T2DM ilişkili SNP olaylarının frekansı genom düzeyinde rastgeleleştirmeanaliziyle incelenebilir. SNP olayları ile eşleştirilen genlere dayanarak, moleküler etkileşim düzeyindeki etkiler incelenebilir.

Gereç ve Yöntemler: İnsan genomu için rastgele SNP olayları oluşturuldu. Farklı T2DM ilişkili SNP olaylarının frekansları incelendi.SNP olaylarıyla eşleştirilmiş genler elde edildi ve bu genlerin doğrudan moleküler etkileşim komşuları incelendi. Ayrıca İnsülinSinyalleşmesi (IS) Yolağının, T2DM ilişkili SNP olaylarının hedefinde olup olmadığı kontrol edildi.

Bulgular: En az bir adet T2DM ilişkili SNP’nin rastgele olarak gözlemlenmesi olası olarak bulundu. Ağ komşuluğu göz önüne alındığında,rastgele SNP olaylarının etkisi genişledi. Bazı SNP’ler ve bunlarla eşleştirilmiş olan genler, diğerlerine göre daha sıkça hedeflendi. ISYolağı üyeleri nadiren hedeflendi, ancak ağ komşuluğu bu yolağa olan etkiyi de genişletti.

Sonuç: Bireysel genomlarda rastgele beklenen varyasyonlar diyabet duyarlılığını muhtemelen etkiler. Ağ düzeyi etkileşimler genomikvaryasyonların etkisini genişletir.

Keywords

Diyabet, Tek Nükleotid Polimorfizmi, Sistem Biyolojisi, Biyolojik Ağlar, Sistem Tıbbı

Random SNP Events for Diabetes and Their Molecular Interaction Neighborhood

Abstract

Aim: Genomics studies provide a collection of Single Nucleotide Polymorphisms (SNPs) which are significantly associated with various diseases and other conditions, including type 2 diabetes mellitus (T2DM). Frequency of T2DM associated SNP events could be investigated by genome scale randomizations. Based on mapped genes for SNP events, molecular interaction neighborhood could also be analyzed.

Material and Methods: Random SNP events were generated for the human genome. Frequencies of different unique T2DM associated SNP events were analyzed. Mapped genes for the SNPs were collected and their direct molecular interaction neighborhood was analyzed. Insulin Signaling (IS) Pathway was also checked to observe if it was targeted by the T2DM associated SNPs.

Results: Having at least a single T2DM associated SNP randomly, was observed to be likely. The effect of random SNP events expanded, when network neighborhood was considered. Some SNPs and their mapped genes were more frequently targeted than others. Although IS Pathway members were rarely targeted, network neighborhood also expanded the influence on IS Pathway.

Conclusion: Randomly expected variations in individual genomes are likely to affect diabetes susceptibility. Consideration of network level relationships enlarge the effect of the genomic variations.

Keywords

Diabetes, Single Nucleotide Polymorphism, Systems Biology, Biological Networks, Systems Medicine

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