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Spatiotemporal dynamics of the postnatal developing primate brain transcriptome.

Bakken TE, Miller JA, Luo R, Bernard A, Bennett JL, Lee CK, Bertagnolli D, Parikshak NN, Smith KA, Sunkin SM, Amaral DG, Geschwind DH, Lein ES - Hum. Mol. Genet. (2015)

Bottom Line: Neocortex showed significantly greater differential expression over time than subcortical structures, and this trend likely reflects the protracted postnatal development of the cortex.In particular, one module with high expression in neonatal cortex and striatum that decreases during infancy and juvenile development was significantly enriched for autism spectrum disorder (ASD)-related genes.This network was enriched for genes associated with axon guidance and interneuron differentiation, consistent with a disruption in the formation of functional cortical circuitry in ASD.

View Article: PubMed Central - PubMed

Affiliation: Allen Institute for Brain Science, Seattle, WA, USA.

No MeSH data available.


Related in: MedlinePlus

WGCNA identified modules predominately associated with region and age. Heatmap plot shows the correlation between MEs and age (A) or region (B). Modules were hierarchically clustered and sorted along the x-axis based on their eigengene–trait relationships. Traits were likewise sorted along the y-axis. Red indicates positive correlation, while blue shows negative correlation.
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DDV166F3: WGCNA identified modules predominately associated with region and age. Heatmap plot shows the correlation between MEs and age (A) or region (B). Modules were hierarchically clustered and sorted along the x-axis based on their eigengene–trait relationships. Traits were likewise sorted along the y-axis. Red indicates positive correlation, while blue shows negative correlation.

Mentions: To identify transcriptional mechanisms in an unbiased manner, we applied weighted gene co-expression network analysis (WGCNA) on the 20 000 most variable probes to identify groups (or ‘modules’) of genes with similar patterns of expression across samples (38) (see Supplementary Material, Table S4 for module assignments and Supplementary Material, Fig. S1). We identified 27 modules that spanned a wide range of spatiotemporal patterns and each was represented using the module eigengene (ME) (the first principle component of each module). To relate these modules to developmental stage or brain region, we correlated each ME against age and region (see Materials and Methods) and hierarchically clustered modules to highlight shared temporal (Fig. 3A) and regional (Fig. 3B) profiles. Consistent with our differential expression analyses, samples from 0 months were most distinct from other ages, with a gross division between modules enriched at early versus late ages (Fig. 3A). Also consistent with our DE analysis, the two neocortical regions clustered closely together and distinctly from the other three brain regions (Fig. 3B). However, the association of modules with regions was complex, and individual modules showed enriched expression in distinct combinations of regions.Figure 3.


Spatiotemporal dynamics of the postnatal developing primate brain transcriptome.

Bakken TE, Miller JA, Luo R, Bernard A, Bennett JL, Lee CK, Bertagnolli D, Parikshak NN, Smith KA, Sunkin SM, Amaral DG, Geschwind DH, Lein ES - Hum. Mol. Genet. (2015)

WGCNA identified modules predominately associated with region and age. Heatmap plot shows the correlation between MEs and age (A) or region (B). Modules were hierarchically clustered and sorted along the x-axis based on their eigengene–trait relationships. Traits were likewise sorted along the y-axis. Red indicates positive correlation, while blue shows negative correlation.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4492396&req=5

DDV166F3: WGCNA identified modules predominately associated with region and age. Heatmap plot shows the correlation between MEs and age (A) or region (B). Modules were hierarchically clustered and sorted along the x-axis based on their eigengene–trait relationships. Traits were likewise sorted along the y-axis. Red indicates positive correlation, while blue shows negative correlation.
Mentions: To identify transcriptional mechanisms in an unbiased manner, we applied weighted gene co-expression network analysis (WGCNA) on the 20 000 most variable probes to identify groups (or ‘modules’) of genes with similar patterns of expression across samples (38) (see Supplementary Material, Table S4 for module assignments and Supplementary Material, Fig. S1). We identified 27 modules that spanned a wide range of spatiotemporal patterns and each was represented using the module eigengene (ME) (the first principle component of each module). To relate these modules to developmental stage or brain region, we correlated each ME against age and region (see Materials and Methods) and hierarchically clustered modules to highlight shared temporal (Fig. 3A) and regional (Fig. 3B) profiles. Consistent with our differential expression analyses, samples from 0 months were most distinct from other ages, with a gross division between modules enriched at early versus late ages (Fig. 3A). Also consistent with our DE analysis, the two neocortical regions clustered closely together and distinctly from the other three brain regions (Fig. 3B). However, the association of modules with regions was complex, and individual modules showed enriched expression in distinct combinations of regions.Figure 3.

Bottom Line: Neocortex showed significantly greater differential expression over time than subcortical structures, and this trend likely reflects the protracted postnatal development of the cortex.In particular, one module with high expression in neonatal cortex and striatum that decreases during infancy and juvenile development was significantly enriched for autism spectrum disorder (ASD)-related genes.This network was enriched for genes associated with axon guidance and interneuron differentiation, consistent with a disruption in the formation of functional cortical circuitry in ASD.

View Article: PubMed Central - PubMed

Affiliation: Allen Institute for Brain Science, Seattle, WA, USA.

No MeSH data available.


Related in: MedlinePlus