بررسی الگوهای ساختاری در شبکه بیان ژنی بیماران وسواسی

رشنو, معصومه; بساق زاده, فاطمه; خوشنود, زهرا

doi:10.48305/jims.v43.i813.0429

بررسی الگوهای ساختاری در شبکه بیان ژنی بیماران وسواسی

نوع مقاله : مقاله های پژوهشی

نویسندگان

¹ استادیار، گروه زیست‌شناسی، واحد دزفول، دانشگاه آزاد اسلامی، دزفول، ایران.

² استادیار، گروه زیست‌شناسی، واحد دزفول، دانشگاه آزاد اسلامی، دزفول، ایران

10.48305/jims.v43.i813.0429

چکیده

مقاله پژوهشی

مقدمه: اختلال وسواس، یک وضعیت روانپزشکی چندوجهی است که با افکار مزاحم و رفتارهای اجباری مشخص می‌شود. عوامل ژنتیکی، یکی از علل اصلی ایجاد‌کننده‌ی این اختلال می‌باشد. این مطالعه با بررسی الگوهای ساختاری در شبکه‌ی بیان ژن بیماران، از یک تحلیل شبکه هم بیان ژن وزنی جامع (WGCNA) برای روشن کردن مکانیسم‌های مولکولی زیربنایی این اختلال استفاده می‌کند.
روش‌ها: این مطالعه بصورت تحلیلی بر روی داده‌های بیان ژنی در بیماران دچار اختلال وسواس در مقایسه با گروه شاهد (افراد سالم) انجام گردید. برای این منظور پس از جستجو در پایگاه‌های داد‌ه‌های بیوانفورماتیکی NCBI، Genercards، Swiss-Prot وDiseasome ژن‌های دخیل در بیماری را که بر اساس حداقل یکی از روش‌های in vivo، in vitro و in silico پیشنهاد شده‌اند، استخراج و به عنوان ژن‌های کاندید در نظر گرفته شد. سپس از مطالعات in vivo، in vitro و نیز in silico داده‌های بیانی جمع‌آوری گردید و شبکه‌های ارتباطی از داد‌های بیانی ژن‌های کاندید به کمک نرم‌افزار MATLAB و R رسم شد.
یافته‌ها: بر اساس نتایج تحلیل شبکه ارتباطی پروتئین‌های کاندید، به کمک 5 شاخص مؤلفه‌ی بیشترین میزان همسایگی، درجه، بین، نزدیکی و شعاع به ترتیب 6 پروتئین SOX9، ACAN، RUNX2، COL1A1، COL2A1 و MMP13 بیشترین میزان تکرار و تأیید توسط این 5 شاخص را داشتند.
نتیجه‌گیری: الگوهای ساختاری در شبکه‌ی بیان ژن نشان داد که ژن‌های SOX9، ACAN، RUNX2، COL1A1، COL2A1 و MMP13 در بیماران وسواسی دارای اهمیت ویژه‌ای نسبت به سایر ژن‌های کاندید هستند. این موضوع می‌تواند در تهیه و آنالیز پانل ژنتیکی تشخیصی این اختلال کمک‌کننده باشد.

تازه های تحقیق

زهرا خوشنود: Google Scholar, PubMed

کلیدواژه‌ها

موضوعات

پزشکی

عنوان مقاله [English]

Investigation of Structural Patterns in the Gene Expression Network of Patients with Obsessive-Compulsive Disorder

نویسندگان [English]

Masoumeh Rashno ¹
Fatemeh Bossaghzadeh ²
Zahra Khoshnood ²

¹ Assistant Professor, Department of Biology, Dezful Branch, Islamic Azad University, Dezful, Iran

² Assistant Professor, Department of Biology, Dezful Branch, Islamic Azad University, Dezful, Iran

چکیده [English]

Background: Obsessive compulsive disorder is a multifaceted psychiatric condition characterized by intrusive thoughts and compulsive behaviors. Genetic factors are one of the main causes of this disorder. By examining the structural patterns in the gene expression network of patients, this study uses a comprehensive weighted gene co-expression network analysis (WGCNA) to clarify the underlying molecular mechanisms of this disorder.
Methods: This study was done analytically on gene expression data from obsessive-compulsive disorder patients compared to a control group (healthy people). Candidate genes involved in the disease, proposed based on at least one method (in vivo, in vitro, or in silico), were extracted from the bioinformatics databases NCBI, GeneCards, Swiss-Prot, and Diseasome. Then, expression data were collected from in vivo, in vitro and in silico studies, and communication networks were drawn from the expression data of candidate genes using MATLAB and R software.
Findings: Based on the results of candidate protein communication network analysis, with the help of 5 component indicators of the highest degree of neighborhood, degree, distance, proximity and radius respectively, the 6 proteins SOX9, ACAN, RUNX2, COL1A1, COL2A1 and MMP13 had the highest frequency of being identified by these 5 metrics.
Conclusion: The structural patterns in the gene expression network showed that SOX9, ACAN, RUNX2, COL1A1, COL2A1, and MMP13 genes are particularly important in obsessive-compulsive patients compared to other candidate genes. This finding could aid in the development and analysis of a diagnostic gene panel for this disorder.

کلیدواژه‌ها [English]

Obsessive-compulsive patients
Structural patterns
Gene expression network

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