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Transcriptional maturation of the mouse auditory forebrain.

Hackett TA, Guo Y, Clause A, Hackett NJ, Garbett K, Zhang P, Polley DB, Mirnics K - BMC Genomics (2015)

Bottom Line: Gene expression in the auditory forebrain during postnatal development is in constant flux and becomes increasingly stable with age.Maturational changes are evident at the global through single gene levels.The database generated by this study provides a rich foundation for the identification of novel developmental biomarkers, functional gene pathways, and targeted studies of postnatal maturation in the auditory forebrain.

View Article: PubMed Central - PubMed

Affiliation: Department of Hearing and Speech Sciences, Vanderbilt University School of Medicine, Nashville, TN, USA. troy.a.hackett@vanderbilt.edu.

ABSTRACT

Background: The maturation of the brain involves the coordinated expression of thousands of genes, proteins and regulatory elements over time. In sensory pathways, gene expression profiles are modified by age and sensory experience in a manner that differs between brain regions and cell types. In the auditory system of altricial animals, neuronal activity increases markedly after the opening of the ear canals, initiating events that culminate in the maturation of auditory circuitry in the brain. This window provides a unique opportunity to study how gene expression patterns are modified by the onset of sensory experience through maturity. As a tool for capturing these features, next-generation sequencing of total RNA (RNAseq) has tremendous utility, because the entire transcriptome can be screened to index expression of any gene. To date, whole transcriptome profiles have not been generated for any central auditory structure in any species at any age. In the present study, RNAseq was used to profile two regions of the mouse auditory forebrain (A1, primary auditory cortex; MG, medial geniculate) at key stages of postnatal development (P7, P14, P21, adult) before and after the onset of hearing (~P12). Hierarchical clustering, differential expression, and functional geneset enrichment analyses (GSEA) were used to profile the expression patterns of all genes. Selected genesets related to neurotransmission, developmental plasticity, critical periods and brain structure were highlighted. An accessible repository of the entire dataset was also constructed that permits extraction and screening of all data from the global through single-gene levels. To our knowledge, this is the first whole transcriptome sequencing study of the forebrain of any mammalian sensory system. Although the data are most relevant for the auditory system, they are generally applicable to forebrain structures in the visual and somatosensory systems, as well.

Results: The main findings were: (1) Global gene expression patterns were tightly clustered by postnatal age and brain region; (2) comparing A1 and MG, the total numbers of differentially expressed genes were comparable from P7 to P21, then dropped to nearly half by adulthood; (3) comparing successive age groups, the greatest numbers of differentially expressed genes were found between P7 and P14 in both regions, followed by a steady decline in numbers with age; (4) maturational trajectories in expression levels varied at the single gene level (increasing, decreasing, static, other); (5) between regions, the profiles of single genes were often asymmetric; (6) GSEA revealed that genesets related to neural activity and plasticity were typically upregulated from P7 to adult, while those related to structure tended to be downregulated; (7) GSEA and pathways analysis of selected functional networks were not predictive of expression patterns in the auditory forebrain for all genes, reflecting regional specificity at the single gene level.

Conclusions: Gene expression in the auditory forebrain during postnatal development is in constant flux and becomes increasingly stable with age. Maturational changes are evident at the global through single gene levels. Transcriptome profiles in A1 and MG are distinct at all ages, and differ from other brain regions. The database generated by this study provides a rich foundation for the identification of novel developmental biomarkers, functional gene pathways, and targeted studies of postnatal maturation in the auditory forebrain.

No MeSH data available.


Grand summary of global gene expression in MG and A1 from P7 to adult. (Top) Unsupervised hierarchical clustering of samples by sex, brain region, and age. (Bottom) Heatmap summarizing total gene expression for each sample, arranged in columns by cluster. Each bar represents one gene. Color code denotes expression level
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Fig1: Grand summary of global gene expression in MG and A1 from P7 to adult. (Top) Unsupervised hierarchical clustering of samples by sex, brain region, and age. (Bottom) Heatmap summarizing total gene expression for each sample, arranged in columns by cluster. Each bar represents one gene. Color code denotes expression level

Mentions: The RNAseq data went through multiple stages of thorough quality control as recommended by Guo et al. [39]. Raw data and alignment quality control were performed using QC3 [40], and gene quantification quality control was conducted using MultiRankSeq [41]. Raw data were aligned with TopHat2 [42] against mouse mm10 reference genome, and read counts per gene were obtained using HTSeq [43]. Default settings were used for MultiRankSeq, TopHat2, and HTSeq. Normalized counts (used in all plots) were obtained by normalizing each gene’s count against the sample’s total read count, then multiplying by a constant (1 X 106). Hierarchical clustering analysis and heatmaps were produced using the Heatmap3 [44] package from R (Fig. 1). For all samples, quality control data are contained in Additional file 2: Tables S2 – S3. The raw counts are contained in Additional file 2: Table S4. Differential expression analyses between all postnatal ages and brain regions were performed using MultiRankSeq [41], which combines three independent methods for RNAseq analysis: DESeq [45]; EdgeR [46]; BaySeq [47]. These three methods were chosen based on results of several previous studies in which multiple RNAseq differential analysis methods were compared for accuracy and sensitivity of read count-based data [48–52]. In analyses of the same dataset, the methods typically differ in numbers of differentially expressed genes identified in a comparison of any two samples, and also in direction of expression (up- or down-regulation). The false discovery rate (FDR < 0.05) was used to correct for multiple testing, and a given comparison was considered to be significant if all three methods identified it as significant. The differential expression data associated with each pairwise comparison (4 ages X 2 brain areas) are summarized in the Results section, with complete data for all genes for all comparisons contained in Additional files 3, 4, 5, 6, 7: Tables S5 – S20. These Additional files are Excel workbooks, organized by tabs corresponding to each supplementary Table. Within each of these files, the listing of single genes is ordered from the smallest to highest numerical ranking (i.e., highest to lowest degree of differential expression), based on p-values from DESeq, EdgeR, and BaySeq. The order can be changed with sorting and filtering functions in Excel.Fig. 1


Transcriptional maturation of the mouse auditory forebrain.

Hackett TA, Guo Y, Clause A, Hackett NJ, Garbett K, Zhang P, Polley DB, Mirnics K - BMC Genomics (2015)

Grand summary of global gene expression in MG and A1 from P7 to adult. (Top) Unsupervised hierarchical clustering of samples by sex, brain region, and age. (Bottom) Heatmap summarizing total gene expression for each sample, arranged in columns by cluster. Each bar represents one gene. Color code denotes expression level
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4536593&req=5

Fig1: Grand summary of global gene expression in MG and A1 from P7 to adult. (Top) Unsupervised hierarchical clustering of samples by sex, brain region, and age. (Bottom) Heatmap summarizing total gene expression for each sample, arranged in columns by cluster. Each bar represents one gene. Color code denotes expression level
Mentions: The RNAseq data went through multiple stages of thorough quality control as recommended by Guo et al. [39]. Raw data and alignment quality control were performed using QC3 [40], and gene quantification quality control was conducted using MultiRankSeq [41]. Raw data were aligned with TopHat2 [42] against mouse mm10 reference genome, and read counts per gene were obtained using HTSeq [43]. Default settings were used for MultiRankSeq, TopHat2, and HTSeq. Normalized counts (used in all plots) were obtained by normalizing each gene’s count against the sample’s total read count, then multiplying by a constant (1 X 106). Hierarchical clustering analysis and heatmaps were produced using the Heatmap3 [44] package from R (Fig. 1). For all samples, quality control data are contained in Additional file 2: Tables S2 – S3. The raw counts are contained in Additional file 2: Table S4. Differential expression analyses between all postnatal ages and brain regions were performed using MultiRankSeq [41], which combines three independent methods for RNAseq analysis: DESeq [45]; EdgeR [46]; BaySeq [47]. These three methods were chosen based on results of several previous studies in which multiple RNAseq differential analysis methods were compared for accuracy and sensitivity of read count-based data [48–52]. In analyses of the same dataset, the methods typically differ in numbers of differentially expressed genes identified in a comparison of any two samples, and also in direction of expression (up- or down-regulation). The false discovery rate (FDR < 0.05) was used to correct for multiple testing, and a given comparison was considered to be significant if all three methods identified it as significant. The differential expression data associated with each pairwise comparison (4 ages X 2 brain areas) are summarized in the Results section, with complete data for all genes for all comparisons contained in Additional files 3, 4, 5, 6, 7: Tables S5 – S20. These Additional files are Excel workbooks, organized by tabs corresponding to each supplementary Table. Within each of these files, the listing of single genes is ordered from the smallest to highest numerical ranking (i.e., highest to lowest degree of differential expression), based on p-values from DESeq, EdgeR, and BaySeq. The order can be changed with sorting and filtering functions in Excel.Fig. 1

Bottom Line: Gene expression in the auditory forebrain during postnatal development is in constant flux and becomes increasingly stable with age.Maturational changes are evident at the global through single gene levels.The database generated by this study provides a rich foundation for the identification of novel developmental biomarkers, functional gene pathways, and targeted studies of postnatal maturation in the auditory forebrain.

View Article: PubMed Central - PubMed

Affiliation: Department of Hearing and Speech Sciences, Vanderbilt University School of Medicine, Nashville, TN, USA. troy.a.hackett@vanderbilt.edu.

ABSTRACT

Background: The maturation of the brain involves the coordinated expression of thousands of genes, proteins and regulatory elements over time. In sensory pathways, gene expression profiles are modified by age and sensory experience in a manner that differs between brain regions and cell types. In the auditory system of altricial animals, neuronal activity increases markedly after the opening of the ear canals, initiating events that culminate in the maturation of auditory circuitry in the brain. This window provides a unique opportunity to study how gene expression patterns are modified by the onset of sensory experience through maturity. As a tool for capturing these features, next-generation sequencing of total RNA (RNAseq) has tremendous utility, because the entire transcriptome can be screened to index expression of any gene. To date, whole transcriptome profiles have not been generated for any central auditory structure in any species at any age. In the present study, RNAseq was used to profile two regions of the mouse auditory forebrain (A1, primary auditory cortex; MG, medial geniculate) at key stages of postnatal development (P7, P14, P21, adult) before and after the onset of hearing (~P12). Hierarchical clustering, differential expression, and functional geneset enrichment analyses (GSEA) were used to profile the expression patterns of all genes. Selected genesets related to neurotransmission, developmental plasticity, critical periods and brain structure were highlighted. An accessible repository of the entire dataset was also constructed that permits extraction and screening of all data from the global through single-gene levels. To our knowledge, this is the first whole transcriptome sequencing study of the forebrain of any mammalian sensory system. Although the data are most relevant for the auditory system, they are generally applicable to forebrain structures in the visual and somatosensory systems, as well.

Results: The main findings were: (1) Global gene expression patterns were tightly clustered by postnatal age and brain region; (2) comparing A1 and MG, the total numbers of differentially expressed genes were comparable from P7 to P21, then dropped to nearly half by adulthood; (3) comparing successive age groups, the greatest numbers of differentially expressed genes were found between P7 and P14 in both regions, followed by a steady decline in numbers with age; (4) maturational trajectories in expression levels varied at the single gene level (increasing, decreasing, static, other); (5) between regions, the profiles of single genes were often asymmetric; (6) GSEA revealed that genesets related to neural activity and plasticity were typically upregulated from P7 to adult, while those related to structure tended to be downregulated; (7) GSEA and pathways analysis of selected functional networks were not predictive of expression patterns in the auditory forebrain for all genes, reflecting regional specificity at the single gene level.

Conclusions: Gene expression in the auditory forebrain during postnatal development is in constant flux and becomes increasingly stable with age. Maturational changes are evident at the global through single gene levels. Transcriptome profiles in A1 and MG are distinct at all ages, and differ from other brain regions. The database generated by this study provides a rich foundation for the identification of novel developmental biomarkers, functional gene pathways, and targeted studies of postnatal maturation in the auditory forebrain.

No MeSH data available.