Sterratt David C
Institute for Adaptive and Neural Computation, School of Informatics, University of Edinburgh, Edinburgh, Scotland, United Kingdom.
PLoS One. 2013 Jun 27;8(6):e67096. doi: 10.1371/journal.pone.0067096. Print 2013.
During the development of the topographic map from vertebrate retina to superior colliculus (SC), EphA receptors are expressed in a gradient along the nasotemporal retinal axis. Their ligands, ephrin-As, are expressed in a gradient along the rostrocaudal axis of the SC. Countergradients of ephrin-As in the retina and EphAs in the SC are also expressed. Disruption of any of these gradients leads to mapping errors. Gierer's (1981) model, which uses well-matched pairs of gradients and countergradients to establish the mapping, can account for the formation of wild type maps, but not the double maps found in EphA knock-in experiments. I show that these maps can be explained by models, such as Gierer's (1983), which have gradients and no countergradients, together with a powerful compensatory mechanism that helps to distribute connections evenly over the target region. However, this type of model cannot explain mapping errors found when the countergradients are knocked out partially. I examine the relative importance of countergradients as against compensatory mechanisms by generalising Gierer's (1983) model so that the strength of compensation is adjustable. Either matching gradients and countergradients alone or poorly matching gradients and countergradients together with a strong compensatory mechanism are sufficient to establish an ordered mapping. With a weaker compensatory mechanism, gradients without countergradients lead to a poorer map, but the addition of countergradients improves the mapping. This model produces the double maps in simulated EphA knock-in experiments and a map consistent with the Math5 knock-out phenotype. Simulations of a set of phenotypes from the literature substantiate the finding that countergradients and compensation can be traded off against each other to give similar maps. I conclude that a successful model of retinotopy should contain countergradients and some form of compensation mechanism, but not in the strong form put forward by Gierer.
在从脊椎动物视网膜到上丘（SC）的地形图发育过程中，EphA受体沿鼻颞视网膜轴呈梯度表达。它们的配体ephrin - A沿SC的前后轴呈梯度表达。视网膜中的ephrin - A和SC中的EphA的反向梯度也存在。这些梯度中任何一个的破坏都会导致映射错误。吉勒尔（1981）的模型利用匹配良好的梯度和反向梯度对来建立映射，可以解释野生型图谱的形成，但无法解释在EphA基因敲入实验中发现的双图谱。我表明，这些图谱可以由诸如吉勒尔（1983）的模型来解释，该模型具有梯度但没有反向梯度，同时还有一种强大的补偿机制，有助于将连接均匀分布在目标区域。然而，这种类型的模型无法解释当反向梯度被部分敲除时发现的映射错误。我通过推广吉勒尔（1983）的模型来研究反向梯度相对于补偿机制的相对重要性，以便补偿强度是可调节的。单独匹配梯度和反向梯度或者梯度匹配不佳但具有强大的补偿机制，都足以建立有序的映射。当补偿机制较弱时，没有反向梯度的梯度会导致较差的图谱，但添加反向梯度会改善映射。该模型在模拟的EphA基因敲入实验中产生双图谱，并且产生与Math5基因敲除表型一致的图谱。对文献中一组表型的模拟证实了这样一个发现，即反向梯度和补偿可以相互权衡以产生相似的图谱。我的结论是，一个成功的视网膜拓扑模型应该包含反向梯度和某种形式的补偿机制，但不是吉勒尔提出的那种强烈形式。
