Molecular control of cell type diversity in the developing spinal cord

1, During embryonic development, a diverse array of neurons and glia are generated at specific positions along the dorsoventral and rostro-caudal axes of the spinal cord from a common pool of precursor cells. 2. This cell type diversity can be distinguished by the spatially and temporally coordinated expression of several transcription factors that are also linked to cell type specification at a very early stage of spinal cord development. 3, Recent studies have started to uncover that the generation of cell type diversity in the developing spinal cord. Moreover, distinct cell types in the spinal cord appear to be determined by the spatially and temporally coordinated expression of transcription factors. 4. The expression of these factors also appears to be controlled by gradients of factors expressed by ventral and dorsal midline cells, namely Sonic hedgehog and members of the transforming growth factor-beta family. 5, Changes in the competence of precursor cells and local cell interactions may also play important roles in cell type specification within the developing spinal cord.

Trait physiology and crop modelling to link phenotypic complexity to underlying genetic systems

New tools derived from advances in molecular biology have not been widely adopted in plant breeding because of the inability to connect information at gene level to the phenotype in a manner that is useful for selection. We explore whether a crop growth and development modelling framework can link phenotype complexity to underlying genetic systems in a way that strengthens molecular breeding strategies. We use gene-to-phenotype simulation studies on sorghum to consider the value to marker-assisted selection of intrinsically stable QTLs that might be generated by physiological dissection of complex traits. The consequences on grain yield of genetic variation in four key adaptive traits – phenology, osmotic adjustment, transpiration efficiency, and staygreen – were simulated for a diverse set of environments by placing the known extent of genetic variation in the context of the physiological determinants framework of a crop growth and development model. It was assumed that the three to five genes associated with each trait, had two alleles per locus acting in an additive manner. The effects on average simulated yield, generated by differing combinations of positive alleles for the traits incorporated, varied with environment type. The full matrix of simulated phenotypes, which consisted of 547 location-season combinations and 4235 genotypic expression states, was analysed for genetic and environmental effects. The analysis was conducted in stages with gradually increased understanding of gene-to-phenotype relationships, which would arise from physiological dissection and modelling. It was found that environmental characterisation and physiological knowledge helped to explain and unravel gene and environment context dependencies. We simulated a marker-assisted selection (MAS) breeding strategy based on the analyses of gene effects. When marker scores were allocated based on the contribution of gene effects to yield in a single environment, there was a wide divergence in rate of yield gain over all environments with breeding cycle depending on the environment chosen for the QTL analysis. It was suggested that knowledge resulting from trait physiology and modelling would overcome this dependency by identifying stable QTLs. The improved predictive power would increase the utility of the QTLs in MAS. Developing and implementing this gene-to-phenotype capability in crop improvement requires enhanced attention to phenotyping, ecophysiological modelling, and validation studies to test the stability of candidate QTLs.