The complexity of human cognition is associated with evolutionary changes in genes engaged in the computational and connectivity properties of certain brain regions. Such genes are the N-methyl-D-aspartate receptor (NMDAR) genes. The NMDAR is a key member of the synaptic machinery, part of the molecular toolbox enabling neuronal communication. NMDARs endow neurons with non-linear processing capabilities thought necessary for complex brain functions like learning, memory and decision making. Given its importance for cognition, we propose to scrutinize the sequence and expression patterns evolution of NMDAR genes and associate these features to the biophysical properties of neurons and their function.
Aim 1: Genetic analyses and selection inference. We will examine the evolution of NMDAR subunits on (a) the sequence and gene-gene interaction levels and (b) the gene expression levels across brain areas. We will combine population genetics, phylogenetics and bioinformatics to: (a) examine whether positive selection has operated in the NMDAR coding and regulatory sequences and at which time point and (b) scrutinize the expression differences between brain areas, humans and non-human organisms and infer the NMDAR gene-coexpression network.
Aim 2: Functional assessment of evolutionary changes in brain models. We will use computational models to (a) translate the genetic findings into biophysical properties, mainly focusing on the kinetics and the number of these receptors, and (b) assess how these evolutionary changes affect the ability of model circuits to perform challenging tasks. We will simulate two functions undertaken by the respective brain areas: spatial navigation in the HPC3 and rule-learning in the PFC4. Since the HPC is an old, conserved structure, we expect to find fewer genetic changes in the NMDAR compared to its counterpart in the PFC.
