A cell fitness selection model for neuronal survival during development
Authors: Yiqiao Wang, Haohao Wu, Paula Fontanet, Simone Codeluppi, Natalia Akkuratova, Charles Petitpré, Yongtao Xue-Franzén, Karen Niederreither, Anil Sharma, Fabio Da Silva, Glenda Comai, Gulistan Agirman, Domenico Palumberi, Sten Linnarsson, Igor Adameyko, Aziz Moqrich, Andreas Schedl, Gioele La Manno, Saida Hadjab, François Lallemend
Summary:
Developmental cell death plays an important role in the construction of functional neural circuits. In vertebrates, the canonical view proposes a selection of the surviving neurons through stochastic competition for target-derived neurotrophic signals, implying an equal potential for neurons to compete. Here we show an alternative cell fitness selection of neurons that is defined by a specific neuronal heterogeneity code. Proprioceptive sensory neurons that will undergo cell death and those that will survive exhibit different molecular signatures that are regulated by retinoic acid and transcription factors, and are independent of the target and neurotrophins. These molecular features are genetically encoded, representing two distinct subgroups of neurons with contrasted functional maturation states and survival outcome. Thus, in this model, a heterogeneous code of intrinsic cell fitness in neighboring neurons provides differential competitive advantage resulting in the selection of cells with higher capacity to survive and functionally integrate into neural networks.
Source: Nature Communications, 2019; 10 (1)
Summary:
Developmental cell death plays an important role in the construction of functional neural circuits. In vertebrates, the canonical view proposes a selection of the surviving neurons through stochastic competition for target-derived neurotrophic signals, implying an equal potential for neurons to compete. Here we show an alternative cell fitness selection of neurons that is defined by a specific neuronal heterogeneity code. Proprioceptive sensory neurons that will undergo cell death and those that will survive exhibit different molecular signatures that are regulated by retinoic acid and transcription factors, and are independent of the target and neurotrophins. These molecular features are genetically encoded, representing two distinct subgroups of neurons with contrasted functional maturation states and survival outcome. Thus, in this model, a heterogeneous code of intrinsic cell fitness in neighboring neurons provides differential competitive advantage resulting in the selection of cells with higher capacity to survive and functionally integrate into neural networks.
Source: Nature Communications, 2019; 10 (1)