Kutmon Martina, Evelo Chris T, Coort Susan L
Department of Bioinformatics - BiGCaT, NUTRIM School for Nutrition, Toxicology and Metabolism, Maastricht University Maastricht, The Netherlands.
BMC Genomics. 2014 Nov 15;15(1):971. doi: 10.1186/1471-2164-15-971.
Nowadays a broad collection of transcriptomics data is publicly available in online repositories. Methods for analyzing these data often aim at deciphering the influence of gene expression at the process level. Biological pathway diagrams depict known processes and capture the interactions of gene products and metabolites, information that is essential for the computational analysis and interpretation of transcriptomics data.The present study describes a comprehensive network biology workflow that integrates differential gene expression in the human diabetic liver with pathway information by building a network of interconnected pathways. Worldwide, the incidence of type 2 diabetes mellitus is increasing dramatically, and to better understand this multifactorial disease, more insight into the concerted action of the disease-related processes is needed. The liver is a key player in metabolic diseases and diabetic patients often develop non-alcoholic fatty liver disease.
A publicly available dataset comparing the liver transcriptome from lean and healthy vs. obese and insulin-resistant subjects was selected after a thorough analysis. Pathway analysis revealed seven significantly altered pathways in the WikiPathways human pathway collection. These pathways were then merged into one combined network with 408 gene products, 38 metabolites and 5 pathway nodes. Further analysis highlighted 17 nodes present in multiple pathways, and revealed the connections between different pathways in the network. The integration of transcription factor-gene interactions from the ENCODE project identified new links between the pathways on a regulatory level. The extension of the network with known drug-target interactions from DrugBank allows for a more complete study of drug actions and helps with the identification of other drugs that target proteins up- or downstream which might interfere with the action or efficiency of a drug.
The described network biology workflow uses state-of-the-art pathway and network analysis methods to study the rewiring of the diabetic liver. The integration of experimental data and knowledge on disease-affected biological pathways, including regulatory elements like transcription factors or drugs, leads to improved insights and a clearer illustration of the overall process. It also provides a resource for building new hypotheses for further follow-up studies. The approach is highly generic and can be applied in different research fields.
如今，大量转录组学数据在在线数据库中公开可用。分析这些数据的方法通常旨在解读基因表达在过程层面的影响。生物通路图描绘了已知过程，并捕捉基因产物和代谢物之间的相互作用，这些信息对于转录组学数据的计算分析和解释至关重要。本研究描述了一种全面的网络生物学工作流程，该流程通过构建相互连接的通路网络，将人类糖尿病肝脏中的差异基因表达与通路信息整合在一起。在全球范围内，2型糖尿病的发病率正在急剧上升，为了更好地理解这种多因素疾病，需要更深入地了解疾病相关过程的协同作用。肝脏是代谢疾病中的关键器官，糖尿病患者常并发非酒精性脂肪性肝病。
经过全面分析，选择了一个公开可用的数据集，该数据集比较了瘦且健康与肥胖且胰岛素抵抗受试者的肝脏转录组。通路分析显示，在WikiPathways人类通路集合中有7条通路发生了显著改变。然后，这些通路被合并为一个包含408个基因产物、(38)种代谢物和(5)个通路节点的组合网络。进一步分析突出了多个通路中存在的(17)个节点，并揭示了网络中不同通路之间的联系。整合来自ENCODE项目的转录因子 - 基因相互作用，在调控层面确定了通路之间的新联系。用DrugBank中已知的药物 - 靶点相互作用扩展网络，有助于更全面地研究药物作用，并有助于识别靶向上下游蛋白质的其他药物，这些药物可能会干扰某种药物的作用或疗效。
所描述的网络生物学工作流程使用了最先进的通路和网络分析方法来研究糖尿病肝脏的重布线。将实验数据与关于疾病影响的生物通路的知识（包括转录因子或药物等调控元件）整合在一起，能带来更深入的见解，并更清晰地阐释整个过程。它还为构建新的假设以供进一步的后续研究提供了资源。该方法具有高度通用性，可应用于不同的研究领域。
