Chang S Laura, Cavnar Stephen P, Takayama Shuichi, Luker Gary D, Linderman Jennifer J
Department of Chemical Engineering, University of Michigan, Ann Arbor, Michigan, United States of America.
Department of Biomedical Engineering, University of Michigan, Ann Arbor, Michigan, United States of America.
PLoS One. 2015 Apr 24;10(4):e0123450. doi: 10.1371/journal.pone.0123450. eCollection 2015.
Chemokine gradient formation requires multiple processes that include ligand secretion and diffusion, receptor binding and internalization, and immobilization of ligand to surfaces. To understand how these events dynamically shape gradients and influence ensuing cell chemotaxis, we built a multi-scale hybrid agent-based model linking gradient formation, cell responses, and receptor-level information. The CXCL12/CXCR4/CXCR7 signaling axis is highly implicated in metastasis of many cancers. We model CXCL12 gradient formation as it is impacted by CXCR4 and CXCR7, with particular focus on the three most highly expressed isoforms of CXCL12. We trained and validated our model using data from an in vitro microfluidic source-sink device. Our simulations demonstrate how isoform differences on the molecular level affect gradient formation and cell responses. We determine that ligand properties specific to CXCL12 isoforms (binding to the migration surface and to CXCR4) significantly impact migration and explain differences in in vitro chemotaxis data. We extend our model to analyze CXCL12 gradient formation in a tumor environment and find that short distance, steep gradients characteristic of the CXCL12-γ isoform are effective at driving chemotaxis. We highlight the importance of CXCL12-γ in cancer cell migration: its high effective affinity for both extracellular surface sites and CXCR4 strongly promote CXCR4+ cell migration. CXCL12-γ is also more difficult to inhibit, and we predict that co-inhibition of CXCR4 and CXCR7 is necessary to effectively hinder CXCL12-γ-induced migration. These findings support the growing importance of understanding differences in protein isoforms, and in particular their implications for cancer treatment.
趋化因子梯度的形成需要多个过程，包括配体分泌与扩散、受体结合与内化以及配体固定于表面。为了理解这些事件如何动态塑造梯度并影响随后的细胞趋化性，我们构建了一个多尺度混合基于代理的模型，将梯度形成、细胞反应和受体水平信息联系起来。CXCL12/CXCR4/CXCR7信号轴与许多癌症的转移高度相关。我们对受CXCR4和CXCR7影响的CXCL12梯度形成进行建模，特别关注CXCL12的三种表达量最高的异构体。我们使用来自体外微流控源-汇装置的数据对模型进行训练和验证。我们的模拟展示了分子水平上异构体差异如何影响梯度形成和细胞反应。我们确定CXCL12异构体特有的配体特性（与迁移表面和CXCR4的结合）对迁移有显著影响，并解释了体外趋化性数据中的差异。我们扩展模型以分析肿瘤环境中的CXCL12梯度形成，发现CXCL12-γ异构体特有的短距离、陡峭梯度有效地驱动趋化性。我们强调了CXCL12-γ在癌细胞迁移中的重要性：其对细胞外表面位点和CXCR4的高有效亲和力强烈促进CXCR4+细胞迁移。CXCL12-γ也更难被抑制，并且我们预测联合抑制CXCR4和CXCR7对于有效阻碍CXCL12-γ诱导的迁移是必要的。这些发现支持了理解蛋白质异构体差异，特别是它们对癌症治疗的影响的重要性日益增加。
