Laboratory of Cellular Biochemistry, Biochemistry Department, Institute of Health Sciences (ICBS), Federal University of Rio Grande do Sul (UFRGS), Porto Alegre, RS, 90035-003, Brazil.
Pharmacology Department, Institute of Health Sciences (ICBS), Federal University of Rio Grande do Sul (UFRGS), Porto Alegre, RS, 90035-003, Brazil.
Alzheimers Res Ther. 2018 Jun 23;10(1):59. doi: 10.1186/s13195-018-0394-7.
Alzheimer's disease (AD) is a multifactorial and complex neuropathology that involves impairment of many intricate molecular mechanisms. Despite recent advances, AD pathophysiological characterization remains incomplete, which hampers the development of effective treatments. In fact, currently, there are no effective pharmacological treatments for AD. Integrative strategies such as transcription regulatory network and master regulator analyses exemplify promising new approaches to study complex diseases and may help in the identification of potential pharmacological targets.
In this study, we used transcription regulatory network and master regulator analyses on transcriptomic data of human hippocampus to identify transcription factors (TFs) that can potentially act as master regulators in AD. All expression profiles were obtained from the Gene Expression Omnibus database using the GEOquery package. A normal hippocampus transcription factor-centered regulatory network was reconstructed using the ARACNe algorithm. Master regulator analysis and two-tail gene set enrichment analysis were employed to evaluate the inferred regulatory units in AD case-control studies. Finally, we used a connectivity map adaptation to prospect new potential therapeutic interventions by drug repurposing.
We identified TFs with already reported involvement in AD, such as ATF2 and PARK2, as well as possible new targets for future investigations, such as CNOT7, CSRNP2, SLC30A9, and TSC22D1. Furthermore, Connectivity Map Analysis adaptation suggested the repositioning of six FDA-approved drugs that can potentially modulate master regulator candidate regulatory units (Cefuroxime, Cyproterone, Dydrogesterone, Metrizamide, Trimethadione, and Vorinostat).
Using a transcription factor-centered regulatory network reconstruction we were able to identify several potential molecular targets and six drug candidates for repositioning in AD. Our study provides further support for the use of bioinformatics tools as exploratory strategies in neurodegenerative diseases research, and also provides new perspectives on molecular targets and drug therapies for future investigation and validation in AD.
阿尔茨海默病（AD）是一种多因素且复杂的神经病理学，涉及许多复杂分子机制的损伤。尽管最近取得了进展，但 AD 的病理生理学特征仍不完全，这阻碍了有效治疗方法的发展。事实上，目前尚无针对 AD 的有效药物治疗方法。转录调控网络和主调控因子分析等综合策略是研究复杂疾病的有前途的新方法，可以帮助识别潜在的药物靶点。
在这项研究中，我们使用转录调控网络和主调控因子分析方法，对人类海马体的转录组数据进行分析，以确定可能在 AD 中作为主调控因子发挥作用的转录因子（TFs）。所有表达谱均使用 GEOquery 包从基因表达综合数据库中获得。使用 ARACNe 算法重建以 TF 为中心的正常海马转录因子调控网络。主调控因子分析和双尾基因集富集分析用于评估 AD 病例对照研究中推断的调控单元。最后，我们使用连接组映射适应来通过药物再利用来预测新的潜在治疗干预措施。
我们确定了一些已经报道与 AD 相关的 TFs，如 ATF2 和 PARK2，以及一些可能成为未来研究目标的新靶点，如 CNOT7、CSRNP2、SLC30A9 和 TSC22D1。此外，连接组映射分析适应表明，六种已获得 FDA 批准的药物可能会调节主调控因子候选调控单元，这些药物可能会重新定位（头孢呋辛、环丙孕酮、地屈孕酮、甲酰胺、三甲双酮和伏立诺他）。
使用以 TF 为中心的调控网络重建，我们能够识别出几个潜在的分子靶点和 AD 中重新定位的六个候选药物。我们的研究进一步支持将生物信息学工具作为神经退行性疾病研究的探索性策略，也为 AD 中未来的分子靶点和药物治疗提供了新的视角。
