Transcriptome-based approach to identify biomimetics for stem cell-derived factors regulating tissue regeneration

基于转录组的方法来识别调节组织再生的干细胞衍生因子的仿生学

• 批准号: 1642363
• 金额: --
• 依托单位: University of Reading
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2015
• 资助国家: 英国
• 起止时间: 2015 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-1642363
• 关键词: Transcriptome; based; approach; identify; biomimetics

Project Details: Stem cells promote the regeneration of numerous tissues in the human body. However the mechanisms underpinning their restorative ability remain to be elucidated. An increasing body of evidence demonstrates that stem cells do not incorporate into newly formed tissue but rather instruct resident cells, via secreted molecules, to activate repair mechanisms. Thus a paradigm shift has been to accept that factors produced by stem cells rather than the stem cells are the efficacious entities controlling tissue repair. This concept represents an unprecedented opportunity to identify and develop compounds, on an industrial scale, that are not only defined but also individually analysed, thus expiditing their safe use to promote tissue regeneration without the involvement of cells.Two major impediments have prevented a systematic approach to identifying biomimetics for secreted stem cells factors. The first was the absence of a robust platform that gives a biological response to stem cells-derived factors. The second was at the other end of the discovery platform; the lack of large number of biologically safe compounds that evoke a change in cell phenotype. Both these hurdles have been recently overcome, firstly through our discovery that human adipose-derived mesenchymal stem cells (ADMSC) secrete factors (SF) that promote cell proliferation, migration and protect against stress-induced senescence. The second development has been the profiling of over 1300 FDA-approved compounds in a variety of cells to develop large data sets that allows each drug to be profiled not on its biological effect but in terms of gene transcription (The Connectivity Map, C-Map). The key innovation is to use bioinformatic approaches to match the changes in transcription evoked by ADMSC-derived factors in their ability to regulate proliferation, migration and senescence to those promulgated by the library of safe compounds, leading to the identification of biomimetics for stem cell-derived factors regulating tissue regeneration.Aims:1. Develop transcriptional profile changes in cell division, cell migration and cell senescence assays through the activity of ADMSC-SF. 2. Perform bioinformatic analysis of transcriptome data and identify activated signalling pathways. 3. Compare the ADMSC SF cell assay transcriptome signatures with the database of FDA compounds [C-Map] to identify, score and rank correlating biomimetics. 4. Investigate biomimetic properties of the 10 strongest compounds in cell division, cell migration and cell senescence assays. 5. Compare ADMSC SF to 3 biomimetics in animal tissue regeneration models.6. Compare ADMSC SF to 3 biomimetics in accelerated aging models.

项目详情：干细胞促进人体内许多组织的再生。然而，支持其恢复能力的机制仍有待阐明。越来越多的证据表明，干细胞并不整合到新形成的组织中，而是通过分泌的分子指导常驻细胞激活修复机制。因此，一个范式转变已经接受了由干细胞而不是干细胞产生的因子是控制组织修复的有效实体。这一概念代表了一个前所未有的机会，以确定和开发的化合物，在工业规模，不仅定义，但也单独分析，从而expiditing他们的安全使用，以促进组织再生，而不涉及celles.Two主要的障碍，阻止了系统的方法来确定仿生分泌的干细胞因子。第一个是缺乏一个强大的平台，可以对干细胞衍生因子产生生物学反应。第二个是在发现平台的另一端;缺乏大量生物安全的化合物，引起细胞表型的变化。这两个障碍最近已经被克服，首先通过我们发现，人脂肪间充质干细胞（ADMSC）分泌的因子（SF），促进细胞增殖，迁移和防止应激诱导的衰老。第二个发展是在各种细胞中对超过1300种FDA批准的化合物进行分析，以开发大型数据集，使每种药物的分析不是基于其生物学效应，而是基于基因转录（连接图，C-Map）。关键的创新是使用生物信息学方法来匹配ADMSC衍生因子在其调节增殖、迁移和衰老的能力中引起的转录变化与安全化合物库所公布的那些相匹配，从而鉴定用于调节组织再生的干细胞衍生因子的仿生物。通过ADMSC-SF的活性开发细胞分裂、细胞迁移和细胞衰老试验中的转录谱变化。2.对转录组数据进行生物信息学分析并识别激活的信号通路。3.将ADMSC SF细胞测定转录组特征与FDA化合物数据库[C-Map]进行比较，以鉴定、评分和排序相关仿生学。4.研究10种最强化合物在细胞分裂、细胞迁移和细胞衰老试验中的仿生特性。5.在动物组织再生模型中比较ADMSC SF与3种仿生剂。在加速老化模型中比较ADMSC SF与3种仿生材料。

项目成果

Attenuation of autophagy impacts on muscle fibre development, starvation induced stress and fibre regeneration following acute injury.

• DOI: 10.1038/s41598-018-27429-7
• 发表时间: 2018-06-13
• 期刊: Scientific reports
• 影响因子: 4.6
• 作者: Paolini A;Omairi S;Mitchell R;Vaughan D;Matsakas A;Vaiyapuri S;Ricketts T;Rubinsztein DC;Patel K
• 通讯作者: Patel K