Okawa Satoshi, Nicklas Sarah, Zickenrott Sascha, Schwamborn Jens C, Del Sol Antonio
Computational Biology Group, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 7 Avenue des Hauts Fourneaux, 4362 Esch-sur-Alzette, Luxembourg.
Developmental and Cellular Biology Group, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 7 Avenue des Hauts Fourneaux, 4362 Esch-sur-Alzette, Luxembourg.
Stem Cell Reports. 2016 Sep 13;7(3):307-315. doi: 10.1016/j.stemcr.2016.07.014. Epub 2016 Aug 18.
Identification of cell-fate determinants for directing stem cell differentiation remains a challenge. Moreover, little is known about how cell-fate determinants are regulated in functionally important subnetworks in large gene-regulatory networks (i.e., GRN motifs). Here we propose a model of stem cell differentiation in which cell-fate determinants work synergistically to determine different cellular identities, and reside in a class of GRN motifs known as feedback loops. Based on this model, we develop a computational method that can systematically predict cell-fate determinants and their GRN motifs. The method was able to recapitulate experimentally validated cell-fate determinants, and validation of two predicted cell-fate determinants confirmed that overexpression of ESR1 and RUNX2 in mouse neural stem cells induces neuronal and astrocyte differentiation, respectively. Thus, the presented GRN-based model of stem cell differentiation and computational method can guide differentiation experiments in stem cell research and regenerative medicine.
识别用于指导干细胞分化的细胞命运决定因素仍然是一项挑战。此外，对于细胞命运决定因素在大型基因调控网络（即GRN基序）中功能重要的子网络中是如何被调控的，我们知之甚少。在此，我们提出了一种干细胞分化模型，其中细胞命运决定因素协同作用以确定不同的细胞身份，并存在于一类被称为反馈环的GRN基序中。基于此模型，我们开发了一种计算方法，该方法可以系统地预测细胞命运决定因素及其GRN基序。该方法能够重现经实验验证的细胞命运决定因素，对两个预测的细胞命运决定因素的验证证实，在小鼠神经干细胞中过表达ESR1和RUNX2分别诱导神经元和星形胶质细胞分化。因此，所提出的基于GRN的干细胞分化模型和计算方法可以指导干细胞研究和再生医学中的分化实验。
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