E2-responsible TRNs in breast cancer


雌激素誘導的乳腺癌過程的基因轉錄調控網路

Date: 15 Jan 2018 (Monday)

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HKBU scholars develop new technology to decode gene transcription facilitating discovery of targeted therapy drugs

浸大學者研發解析基因調控技術新突破 LogicTRN能快速尋找頑疾關鍵調控路徑 推動標靶藥物研發

A research team from the School of Chinese Medicine (SCM) has developed the world’s first model framework and “LogicTRN” algorithm to accurately establish a gene regulatory route to analyse the genetic function and understand the biological processes that are responsible for the development of organs, progression of diseases and other complex biological events such as aging. Such a new mechanism could help efficiently locate the key regulatory route for complicated diseases, thereby facilitating the research and development of targeted therapy drugs. The research team has successfully applied the new integrative approach to analyse breast cancer and characterise the logical relations among transcription factors (TFs) in regulating biological processes. The work was recently published in the prestigious academic journal Nature Communications (https://www.nature.com/articles/s41467-017-01193-0).

Professor Lyu Aiping, Dean of SCM, Professor Zhang Ge, Director of Technology Development Division and Associate Director of Teaching and Research Division (CMTR), and Dr Zhu Hailong, Assistant Professor of CMTR, are leading a bioinformatics team to investigate the gene regulatory mechanisms through computational modeling techniques and big data analytics.

Professor Lyu Aiping said that scientists have generated vast amounts and various types of biological big data; therefore, effective computational approaches must be developed to integrate and analyse such data so as to generate useful information for understanding the core mechanism of biological processes. It was difficult in the past to develop a reliable relationship for gene regulation due to the lack of a theoretical model. “LogicTRN” is a model structure developed based on the biological process in which its parameters have significant meaning for the field of biology and its conclusion could be verified through experiments. Therefore, the application of LogicTRN has key implications on learning more about the gene regulatory mechanism during biological processes.

Dr Zhu Hailong said that TFs are functional proteins that can bind to the promoter of genes to turn on/off the gene expression. To a great extent, the organism can conduct accurate regulation through the gene expression controlled by TFs in order to execute various functions. TFs on the other hand are regulated by its upstream gene, thus creating a very complicated regulatory route. This complicated and combinatorial nature of TF regulation is the core of various cellular processes; it also explains why 2,000 to 3,000 TFs are enough to control the complex spatio-temporal expression of over 30,000 genes in the right cell at the right time and in the right amount throughout the life of the cell and the organism.

Dr Zhu said that “LogicTRN” is an open model framework which can be potentially extended to integrate the influences of various processes such as gene mutation, TF-DNA binding, miRNA regulation, protein translation, and protein-protein-interaction to decode the underlying mechanisms of gene transcription. With the acquisition and accumulation of biological data of more and more cellular processes, data analysis based on “LogicTRN” can enhance and contribute to a comprehensive understanding of molecular interactions in cells. He added that “LogicTRN” was successfully applied to analyse datasets representing the estrogen-induced breast cancer and human-induced pluripotent stem cell (hiPSC)-derived cardiomyocyte (CM) development. The derived networks are consistent with existing knowledge and previous experiments.

Professor Zhang Ge explained that the major problem in the areas of disease research and drug discovery is how to cure targeted genes. Since the gene relationship is extremely complicated, it poses difficulties to the study of crucial genes. The biological informative method that is currently used seems to be a way to identify the correlation between genes, however, the accuracy is still unsatisfactory and yet to be verified. Professor Zhang added that “LogicTRN” provides researchers with a comparatively powerful analytic tool in unravelling the key pathways and new therapeutic targets of complicated diseases such as cancer.

中醫藥學院的研究團隊建立了世界首項基因轉錄調控的模型理論及演算法「LogicTRN」,能有助準確構建基因調控網路,有望用於解析基因功能,幫助理解生物過程諸如器官發育,疾病發生及發展,以及衰老等複雜的核心機制,並快速尋找複雜疾病的關鍵調控路徑,從而推動標靶藥物的研究與開發。研究人員成功應用新方法於分析乳腺癌和心肌細胞發育過程中的基因轉錄調控,並在多個調控模式的實驗中得到驗證。該項研究結果最近於國際著名學術期刊《自然通訊》上發表 (https://www.nature.com/articles/s41467-017-01193-0)。

研究團隊包括中醫藥學院院長呂愛平教授、技術開發部主任及教學科研部副主任張戈教授與教學科研部助理教授祝海龍博士。

呂愛平教授指出,目前科學家正在快速積累海量的生物大數據,因此有必要開發有效的模型及方法,綜合利用及分析各種生物資料,從而理解生物過程的核心機制。過去,由於缺乏相關的理論模型,導致無法構建出可靠的基因調控關係。LogicTRN是一個建基於生物學過程的模型架構,其模型參數具有生物學意義,其結論可以通過實驗來驗證,因此,LogicTRN的應用對於探索生物過程中的基因調控機制具有重要的啟示作用。

祝海龍博士表示,轉錄因子是一類功能蛋白,通過結合在基因啟動子區域對基因轉錄的開啟及終止進行控制,生命體在很大程度上通過轉錄因子對基因表達進行精確調控,從而實現各種功能,而轉錄因子自身又受到其上游基因的調控,因此形成一個複雜的調控網路。這種複雜的組合調控是各種細胞過程的核心,2,000至3,000個轉錄因子,就可以調控超過30,000個基因,使它們在正確的細胞、正確的時間,以正確的數量進行表達。

他說,LogicTRN是一個開放的模型框架,可以同時考慮各個細胞過程,如基因變異、蛋白-DNA結合、miRNA調控、蛋白翻譯及蛋白與蛋白相互作用等對基因轉錄的調控及影響。隨著越來越多的生物過程中的數據採集和積累,LogicTRN提供的數據分析將可增進人類對細胞中各種分子活動的認知。他說,團隊已成功應用新方法於分析雌激素誘導的乳腺癌過程及人工誘導多能幹細胞衍生的心肌細胞發育過程中的基因轉錄調控,並成功在多個調控模式的實驗中得到驗證。

張戈教授說,目前疾病研究及新藥開發中遇到的一個關鍵問題是如何確定治療靶基因。由於基因之間的關係異常複雜,導致在研究關鍵基因過程中往往顧此失彼。現時普遍使用的生物資訊方法,雖能找到基因之間的關聯關係,但準確性不足而且難被驗證。相對而言,LogicTRN更加準確又能作定量分析,是臨床研究的有效分析工具,幫助快速尋找複雜疾病如癌症中的關鍵調控路徑及治療靶基因。