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سومین کنفرانس زیست‌شناسی سامانه‌های ایران ؛ 8 و 9 اسفند ماه 1396
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| عنوان فارسی |
هدف قرار دادن شبکه ی اصلی تنظیم بیان ژنی در سلول های بنیادی سرطان در مدل سلولی AML |
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| چکیده فارسی مقاله |
سرطان به عنوان یکی از عوامل افزایش مرگ و میر ناگهانی است که در هر سال جان تعداد زیادی از افراد را در جهان می گیرد. لوسمی میلوئید حاد بالغین (AML) از شایع ترین سرطان های خون در بزرگسالان است. برای درمان این دسته از سرطان ها، چهار نوع درمان متعارف استفاده می شود که شامل شیمی درمانی، پرتودرمانی، پیوند مغز استخوان و استفاده از آنتی بادی ها و یا مجهز کردن سلول های ایمنی است. مقاومت بیمار به درمان(Drug resistancy) و بازگشت مجدد بیماری بعد از درمان (Relapse) به عنوان دو مشکل اساسی در کلینیک محسوب می شوند. در مطالعات اخیر دیده شده که نرخ بالای مقاومت به پاسخ دارو و بازگشت مجدد بیماری به دلیل حضور دسته ای از سلولی هابا ویژگی خودنوزایی ((Self-renewal هستند که سلول های بنیادی سرطان خون (Leukaemia stem cell) نامیده می شود. یکی از رویکردهایی که اخیرا در درمان سرطان مورد استفاده قرار می گیرد، القاء تمایز (Differentiation therapy) و یا مرگ سلولی در سلول های بنیادی سرطانی است. هدف ما در این مطالعه پیدا کردن ژن های هسته تنظیمی سلول های بنیادی سرطانی است به طوریکه مهار این ژن باعث القاء تمایز و یا مرگ سلولی شود. هدف این مطالعه بررسی شبکه تنظیم بیان ژن (Gene regulatory network-GRN) با استفاده از داده های منتشر شده از بیماران AML به منظور یافتن ژن های اصلی شبکه تنظیم بیان ژن در این سلول هاست. در این مطالعه ابتدا با آنالیز داده های میکرواری منتشر شده (GSE76009) شبکه تنظیم بیان ژن) (GRN در سلول های بنیادی سرطان خون (LSC) ترسیم می شود. با مقایسه ژن های شبکه تنظیمی کاندید شده و مطالعه ژن های منتشر شده در مقالات مختلف، لیستی از ژن های موثر در تنظیم هسته مرکزی این سلول ها معرفی می شود. |
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| کلیدواژه‌های فارسی مقاله |
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| عنوان انگلیسی |
Targeting the core regulatory network of stemness in an in vitro AML model using a GRN approach |
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| چکیده انگلیسی مقاله |
Objective and background A main goal in regard of AML therapy is to tackle two main obstacles ; refractoriness to induction chemotherapy and relapse after remission which may be initiated from a rare population of cells called, Leukemia stem cells (LSCs);Which lead to the rapid clonal expansion of immature myeloblasts. Interestingly, these cells possess a number of stem cell properties such as quiescence and survival that are linked to therapy resistance. Therefore, targeting these cells could eradicate AML. In general, there are three main approaches in targeting LSCs :1) Targeting stem cell related molecular pathways 2) Targeting stem cell specific surface markers 3) Targeting the stem cell microenvironment. In recent studies, it is suggested that applying bioinformatic approaches may have an effective role in identifying precise biomarkers or targets in LSCs. Gene Regulatory Network (GRN) is an advisable tool in choosing specific genes, which may have a high probability in regulating the stemness core in LSCs. GRN is considered as an adequate approach in mapping gene interactions and pinpointing hot spots also known as “hub genes”, that have the most interaction with others. By applying this approach and using a defined set of criteria, we can attain a list of genes and perform further studies on them in wetlab. Methodology Research question: What genes may be considered as key regulators in the stemness and survival pathways of LSCs? A difficult problem is to pinpoint key regulators which result to the invincible presence of LSCs. During the following years many studies have been conducted in identifying precise biomarkers or targets, which can lead to the efficient targeting of these cells. In this study we used GRN; an adequate bioinformatics tool which may be used to attain a list of candidate genes. Therefore, In this study, 5 main steps were preformed: 1) Data collection: a data set of 495 Gene expression profiles (GSE76009) was obtained from GEO database, the following microarray data was generated using Affymetrix Human Genome (HG) U133 Plus 2.0 GeneChips. Data normalization and background correction was conducted using SVA package in R software (3.4.2 version). 2) Differentially expression analysis was performed between two defined groups of LSC+ and LSC- using limma package in R Software with the following cut off; FC>2, Adj.p-val< 0.05. 3) Network reconstruction was conducted using ARACNE algorithm and cytoscape software with two defined cut offs; FC @1.5 and, FC@1.3 .4) Network analysis, in this stage hub genes were attained; the selected genes(hub genes with the most interactions), had a higher DF than the excluded ones. 5) Functional analysis and biological evidences were studied for the candidate genes. Enrichment was preformed on the following genes using Enrichr database and the following genes were confirmed in literature by a defined set of criteria such as; a) the expression level of the candidate genes b) literarture-based functional assays c) self renewal and survival related pathways using signaling pathway databases such as KEGG and PANTHER (Fig1). Figure1. A general workflow of the following steps. Results In result, two networks were attained. The first network was constructed for 534 genes, which consisted a total amount of 31 hub genes (10 DF 25), the second one which was larger was constructed for 925 genes with a total amount of 50 hub genes (20 DF 74). Following the previous step, common genes were obtained between the hub genes (31 hub genes & 50 hub genes) (Fig2). As observed below, 29 genes were found common in both lists. Figure2. The following Venn diagram shows 29 common genes between the two hub gene lists. The following 29 genes were studied through literature and were enriched in enrichR database. In result, 4 genes were enriched (P-val: 3.067e-7, Adj. P-val: 0.00001779) in transcriptional misregulation signaling pathways in AML, leading us to this notion that these candidate genes might play a significant role as key regulators in the stemness core of LSCs. Conclusion The appointed cut offs which were applied in the GRN construction were chosen based upon previous studies. More networks can be constructed using other attained datsets; resulting to more hub genes. It must be also noted that, a practical implication of this data would be to test the following genes in an in vitro AML model to see whether the down regulation of these genes can effect the stemness in LSCs or not. References Hope KJ, Jin L, Dick JE. Acute myeloid leukemia originates from a hierarchy of leukemic stem cell classes that differ in self-renewal capacity. Nat Immunol. 2004;5:738 Li, H., et al., Learning the structure of gene regulatory networks from time series gene expression data. BMC genomics, 2011. 12(5): p.1 Ng, Stanley WK, et al. A 17-gene stemness score for rapid determination of risk in acute leukaemia. Nature 540.7633 (2016): 433-437. Pan, Jia-Qi et al. “lncRNA Co-Expression Network Model for the Prognostic Analysis of Acute Myeloid Leukemia.” International Journal of Molecular Medicine 39.3 (2017): 663–671. PMC. Web. 29 July 2017. |
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| کلیدواژه‌های انگلیسی مقاله |
سرطان خون میلوئیدی- سلول های بنیادی سرطان خونی- شبکه ی تنظیم بیان ژنی- بیوانفورماتیک |
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| نویسندگان مقاله |
مهدی توتونچی |
department of genetics, reproductive biomedicine research center, royan institute for reproductive biomedicine, acecr, tehran, iran and department of stem cells and developmental biology, cell science research center, royan institute for stem cell biology and technology, acecr, tehran, iran
سازمان اصلی تایید شده: پژوهشگاه رویان (Royan institute)
مهسا محمدی |
department of stem cells and developmental biology, university of science and culture, acecr, tehran, iran
سازمان اصلی تایید شده: دانشگاه علم و فرهنگ (University of science and culture)
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| نشانی اینترنتی |
http://icsb2018.modares.ac.ir/browse.php?a_code=A-10-96-1&slc_lang=fa&sid=1 |
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