Gene Expression Switching of Receptor Subunits in Human Brain Development

• Published in: PLoS computational biology Vol. 11; no. 12; p. e1004559
• Main Authors: Bar-Shira, Ossnat, Maor, Ronnie, Chechik, Gal
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01.12.2015; Public Library of Science (PLoS)
• Subjects: Age; Aging - genetics; Brain; Brain - growth & development; Chromosome Mapping - methods; Gene expression; Gene Expression Regulation, Developmental - genetics; Genes, Switch - genetics; Genetic aspects; Humans; Information processing; Methods; Neuroplasticity; Observations; Physiological aspects; Protein Subunits - genetics; Proteins; Receptors, GABA - genetics; Receptors, N-Methyl-D-Aspartate - genetics; Receptors, Neurotransmitter - genetics; Rodents; Signal transduction; Synapses; Synaptic Transmission - genetics; Trends
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1004559

Synaptic receptors in the human brain consist of multiple protein subunits, many of which have multiple variants, coded by different genes, and are differentially expressed across brain regions and developmental stages. The brain can tune the electrophysiological properties of synapses to regulate plasticity and information processing by switching from one protein variant to another. Such condition-dependent variant switch during development has been demonstrated in several neurotransmitter systems including NMDA and GABA. Here we systematically detect pairs of receptor-subunit variants that switch during the lifetime of the human brain by analyzing postmortem expression data collected in a population of donors at various ages and brain regions measured using microarray and RNA-seq. To further detect variant pairs that co-vary across subjects, we present a method to quantify age-corrected expression correlation in face of strong temporal trends. This is achieved by computing the correlations in the residual expression beyond a cubic-spline model of the population temporal trend, and can be seen as a nonlinear version of partial correlations. Using these methods, we detect multiple new pairs of context dependent variants. For instance, we find a switch from GLRA2 to GLRA3 that differs from the known switch in the rat. We also detect an early switch from HTR1A to HTR5A whose trends are negatively correlated and find that their age-corrected expression is strongly positively correlated. Finally, we observe that GRIN2B switch to GRIN2A occurs mostly during embryonic development, presumably earlier than observed in rodents. These results provide a systematic map of developmental switching in the neurotransmitter systems of the human brain.