title: On the Importance of Countergradients for the Development of Retinotopy: Insights from a Generalised Gierer Model.
abstract: 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.
authors.affiliation: Institute for Adaptive and Neural Computation, School of Informatics, University of Edinburgh, Edinburgh, Scotland, United Kingdom.
authors.forename: David C
authors.initials: DC
authors.lastname: Sterratt
mesh_terms: D000818:Animals; D051792:Basic Helix-Loop-Helix Proteins / Q000235:genetics / Q000378:metabolism; D003198:Computer Simulation; D055879:Gene Knock-In Techniques; D055786:Gene Knockout Techniques; D008822:Mice, Transgenic; D008959:Models, Neurological; D009419:Nerve Tissue Proteins / Q000235:genetics / Q000378:metabolism; D009473:Neuronal Plasticity / Q000502:physiology; D009474:Neurons / Q000166:cytology / Q000502:physiology; D010641:Phenotype; D036121:Receptor, EphA3 / Q000235:genetics / Q000378:metabolism; D036081:Receptors, Eph Family / Q000235:genetics / Q000378:metabolism; D012160:Retina / Q000166:cytology / Q000254:growth & development* / Q000502:physiology*; D013477:Superior Colliculi / Q000166:cytology / Q000254:growth & development / Q000502:physiology; D014795:Visual Pathways / Q000166:cytology / Q000254:growth & development / Q000502:physiology
chemical_list: D051792:Basic Helix-Loop-Helix Proteins; D009419:Nerve Tissue Proteins; D036121:Receptor, EphA3; D036081:Receptors, Eph Family; C117638:Atoh7 protein, mouse; C491717:Epha3 protein, mouse