(141a) Use of Computational Biology and Gene Expression Array Analysis to Understand Human Brain Evolution | AIChE

(141a) Use of Computational Biology and Gene Expression Array Analysis to Understand Human Brain Evolution


Grossman, L. I. - Presenter, Wayne State University
Chugani, H., Wayne State University
Kuzawa, C., Northwestern University
Wildman, D. E., Wayne State University School of Medicine
Lipovich, L., Wayne State University
Sherwood, C., George Washington University
Sterner, K., Wayne State University
McGowen, M., Wayne State University

What makes us human? Considering how minor the changes in DNA sequence are between ourselves and our nearest primate relatives, this is a question of abiding interest. We have focused on the brain to address this question in a series of evolutionary genomic studies. The goal is to identify both biological processes and individual genes that have been important during human evolution. We first examined lineages (cetaceans and proboscideans) in addition to human that produced large brained species (dolphins and whales, and elephants). We utilized dN/dS, the ratio of nonsynonymous (amino acid replacing) nucleotide substitutions per nonsynonomous site to synonymous (amino acid unchanging) substitutions per synonymous site, as a measure of positive natural selection and identified the biological categories in which positively selected genes were found. Humans, dolphins, and elephants all showed genes expressed in mitochondria that are significantly overrepresented among positively selected genes. This finding underlines the evolutionarily parallel molecular trajectories of groups possessing a large brain, among the most energy consuming tissues. We then studied age-specific gene expression in human neocortex, which is characterized by protracted developmental intervals of synaptogenesis and myelination that allow for an extended period of learning, taken from the nondiseased margins of surgically resected tissue. We found 8 nonmessenger, long nonprotein-coding ribonucleic acid (lncRNA) molecules that are differentially expressed in an age-dependent way, 337 transcripts with greater inter-individual variance among children than among adults – many annotated to the immune system – and 40 transcripts with significant age-related trajectories in expression. These findings present some of the genetic underpinnings of the extended period of human cortical development with peak brain glucose metabolism between ages 4 and 10 years. (Supported by NSF BCS-0827546)



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