- Human gene expression variability and its dependence on methylation and aging. - differential gene expression. - Conclusions: We conclude that gene expression variability in the human population is likely to be important in tissue development and identity, methylation, and in natural biological aging. - The expression variability of a gene is an important functional characteristic of the gene itself and the classification of a gene as one with Hyper-Variability or Hypo-Variability in a human population or in a specific tissue should be useful in the identification of important genes that functionally regulate development or disease.. - These differences in gene expression lead to phenotypic variability across a popu- lation. - There are several sources of expression variability in a population. - Another source of gene expression variability is plasticity, whereby an organism adjusts gene expression to alter its phenotype in response to a changing environment [6].. - In this analysis, we used a novel method to analyze global gene expression variability in non-diseased human breast, cerebellum, and frontal cortex tissues. - Estimating expression variability. - We measured human gene expression variability (EV) [1] in post-mortem non-diseased cerebellum (n = 465) and frontal cortex samples (n = 455) and biopsied nor- mal breast tissues (n = 144). - Gene expression was mea- sured using the Illumina HumanHT-12 V3.0 expression BeadChip. - Breast tissue exhibited a larger shoulder of the negative EV probes compared to cerebellum and frontal cortex tissues. - A previously unexplored aspect of expression Hyper- variability is the statistical characteristics of expression amongst genes with this wide range of gene expression.. - 1 Expression variability (EV) in human breast, cerebellum, and frontal cortex tissue. - (c) Expression variability as a function of median gene expression. - The remaining majority of Hyper-Variable probes had a unimodal distribution. - We see a substantial decrease in the number of probes in the Hyper- and Hypo-Variable probe sets after conducting our split- retest protocol (Fig. - 2 Bimodal Hyper-Variable gene expression detection. - The dashed lines represent the overall gene kernel density estimation function of gene expression. - Frontal Cortex . - This is an important question because expression variability exists not only between individuals but between different tissues in the same organism. - 4a, only a small minority of Hyper-Variable and Hypo-Variable probe- mapped gene sets are shared between the three tissues.. - 16% of the Hyper-Variable probe-mapped genes were classified as such in the three tissues and 18–26% of the Hypo-Variable were so classified. - The Non-Variable probe-mapped gene sets contained over 82% of genes in each tissue type, with over 71% of the measured genes commonly classified as NV in all three tissue types.. - In order to understand the overall biological significance of EV, we examined the functional aspects that are enriched in the Hyper-Variable, Hypo-Variable, and Non- Variable probe-mapped gene sets by conducting a gene set enrichment analysis in each category. - We conducted a functional enrichment analyses of the gene symbols. - It should be noted that the GO term “Proteolysis involved in cellular catabolism” appears both in the “Common Probe- Mapped Genes” and “Breast-Specific Probe Mapped Genes” for the Hypo-Variable set. - The breast Hyper-Variable probe-mapped gene set was uniquely enriched for epithelial cell differentiation, primary alcohol metabolism, and positive regulation of cellular com- ponent movement. - The cerebellum Hyper-Variable probe- mapped gene set was uniquely enriched for regulation of nervous system development, transmembrane transport, and neuron death. - The frontal cortex Hyper-Variable probe-mapped gene set was enriched for histamine secre- tion, regulation of cell morphogenesis, and trans-synaptic signalling. - The breast, cerebellum, and frontal cortex Hyper-Variable probe-mapped gene sets were commonly enriched for regulation of tissue remodeling, inflammatory responses, and responses to inorganic substances. - Of note, many of the enriched GO annotations of the Hyper- Variable genes are involved in signalling pathways.. - Table 3 Top 5 common and tissue-specific REVIGO GO annotations in the Hyper-Variable and Hypo-Variable probe mapped gene sets of breast, cerebellum, and frontal cortex tissues. - Hypo- Variable. - In the case of the Hypo-Variable probe-mapped gene sets, all three tissue types were enriched for protein ca- tabolism and metabolism, ribonucleoprotein complexes, and negative regulation of autophagy. - In this respect, many of the shared Hypo-Variable genes could be con- sidered housekeeping genes. - significant enrichment of essential genes within the Hyper-Variable probe-mapped gene sets.. - The cerebellum essential Hyper- Variable probe-mapped gene set was enriched for regulation of cell development, epithelial cell migration, positive regulation of cell proliferation, cellular response to growth factor stimulus, and anterograde trans- synaptic signalling. - Overall, the Hyper-Variable essential probe-mapped gene sets tended to be enriched for morphogenic, tissue, and organ system development.. - DNA methylation and expression variability. - While the relationship between methylation and gene expression is complex, low pro- moter methylation is associated with high levels of gene expression [31–34]. - Frontal Cortex Hyper . - Genes were classified as Medium Methylated for those in the. - 5b), Table 5 Top 5 common and unique REVIGO GO annotation subsets of Hyper-Variable and Hypo-Variable essential genes in breast, cerebellum, and frontal cortex tissues. - Hyper-Variable Essential Genes. - we observe that Hypo-Variable genes have a visibly dif- ferent methylation pattern than Hyper- or Non-Variable genes insofar as Hypo-Variable genes are visibly overrep- resented in the Non-Methylated gene group compared to both the Hyper-Variable and Non-Variable genes.. - To further quantify the overrepresentation of Hypo- Variable genes in the Non-Methylated gene group, we conducted a chi-squared test of independence between the methylation state clusters and the EV classifications (Table 6 and Additional file 4). - By examining the standardized residuals of the chi-square test of independence, we quantitatively confirmed the enrichment of Non-Methylated genes within the Hypo- Variable probe-mapped gene set. - We also observe a significant enrichment of Highly Methylated genes in the Non-Variable gene set as well as an enrichment of Medium Methylated genes in the Hyper-Variable probe- mapped gene set. - of Hyper-Variable frontal cortex probes. - In the cerebellum, there were 247 Hyper-Variable probes whose expression increased as a function of age and 267 genes with decreased expression. - Given that age is corre- lated with a considerable number of Hyper-Variable probes, we classified the age of the samples in the cere- bellum and frontal cortex tissues into three age clusters according to BIC for expectation-maximization (EM) initialized by hierarchical clustering for parameterized Gaussian mixture models. - To further explore this effect, we examined the age- dependent changes in expression of the Hyper-Variable probes across the three clusters. - Hypo-Variable . - Hyper-Variable . - The dark orange cluster in the. - Lastly, we see a random scattering of expression in the yellow cluster of the frontal cortex heat- map that steadily increases with age. - Most of the downregulated age-dependent Hyper- Variable genes in the cerebellum fall into the green cluster where expression of the genes in the cluster increases with age. - Overall, the func- tional annotations of the age-regulated Hyper-Variable gene clusters suggest that population EV is one outcome of age-dependent gene expression changes.. - We next investigated a possible impact of methylation status on gene expression in the Up- and Down- regulated Hyper-Variable genes. - 6 Hierarchical clustering of Hyper-Variable genes by age in (A) cerebellum tissue, and (B) frontal cortex tissue. - gene expression and gene methylation for each gene. - We observe no strong correlation between expression and methylation, suggesting age-dependent changes in ex- pression of the age-regulated Hyper-Variable genes are not the result of methylation changes.. - Gene expression variability in a population is the cumu- lative result of intrinsic genetic factors, extrinsic envir- onmental factors, and stochastic noise. - In this report, we study population gene expression variability in human breast, cerebellum, and frontal cortex tissues.. - Our investigation into human gene expression variabil- ity yielded several main findings. - Lastly, we find that only a small number of Hyper-Variable. - With respect to expression variance, cell type heterogeneity is likely to manifest itself in the identification of a gene as Hyper- Variable based on the fluctuating presence of a cell type with a unique gene expression profile. - This could be one explanation for the presence of cell-type specific process in the Hyper-Variable genes associated with aging (e.g.. - Glial Cell Differentiation) or in the Frontal cortex- specific Hyper-Variable genes (e.g. - Secondly, we identified common Hyper-Variable genes between the breast, cere- bellum and frontal cortex. - Histogram of Pearson correlation coefficient between paired gene expression and gene methylation levels in the Hyper-Variable and Hypo-Variable probe sets. - Shared GO annotations provided by the functional enrichment analysis of the Hypo- Variable probe-mapped genes in breast and brain tissues (Table 3) indicate that many of these genes are likely to have housekeeping functions. - The enrichment of essential genes in the Hypo- Variable probe-mapped gene sets is in agreement with previous findings in yeast showing that essential yeast genes are likely to have low expression variability. - How- ever, we detected a significant number of essential genes amongst the Hyper-Variable probe-mapped gene sets in breast, cerebellum, and frontal cortex tissue. - One possible explanation would be tissue heterogeneity in the samples (see above). - Firstly, we find that Non-Variable genes in the cerebellum and frontal cortex are likely to have high gene methylation. - This suggests that the Hyper-Variability and age-dependent upregulation of genes associated with glial cell differentiation or an increase in the number of glial cells in the samples.. - Our work shows that gene expression variability in the hu- man population is likely to be important in development, tissue-specific identity, methylation, and in aging. - As such, the EV of a gene is an important feature of the gene itself.. - Illumina gene expression and methylation microarray data. - The second gene expression and the methylation data- sets were catalogued by the North American Brain Ex- pression Consortium and UK Human Brain Expression Database (UKBEC) [37, 59]. - The expression data was ob- tained from the Gene Expression Omnibus (GEO) data- base [60] under accession number GSE36192. - Using the Pearson’s chi-square test, we tested for enrich- ment of essential genes in each probe-mapped gene set relative to the total number of essential genes in the Illu- mina HumanHT-12 V3.0 expression BeadChip. - Hierarchical clustering of age-dependent hyper-variable genes. - Complete list of GO term treemaps for age-regulated Hyper-Variable genes. - EV: Expression Variability. - GEO: Gene Expression Omnibus. - Determinants of expression variability. - Gene expression variability in clonal populations: causes and consequences. - Higher gene expression variability in the more aggressive subtype of chronic lymphocytic leukemia. - Differential variability analysis of gene expression and its application to human diseases.. - Selection to minimise noise in living systems and its implications for the evolution of gene expression. - Positive selection for elevated gene expression noise in yeast. - Gene expression variability within and between human populations and implications toward disease susceptibility.. - Project normal: defining normal variance in mouse gene expression. - Variance of gene expression identifies altered network constraints in neurological disease. - Postmortem interval effect on RNA and gene expression in human BRAIN tissue. - Widespread sex differences in gene expression and splicing in the adult human brain. - Neurogenesis in the aging brain
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