Aging Shapes the Population-Mean and -Dispersion of Gene Expression in Human Brains

Brinkmeyer-Langford, Candice; Guan, Jinting; Ji, Guoli; Cai, James J.

doi:10.3389/fnagi.2016.00183

Cited by 28 publications

(32 citation statements)

References 82 publications

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“…old age 22 . Meanwhile, another study performing single-cell RNA sequencing of human pancreatic cells, identifies an increase in transcriptional heterogeneity and somatic mutations with age 23 .…”

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confidence: 99%

Temporal changes in the gene expression heterogeneity during brain development and aging

Işıldak

Somel

Thornton

et al. 2020

Sci Rep

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cells in largely non-mitotic tissues such as the brain are prone to stochastic (epi-)genetic alterations that may cause increased variability between cells and individuals over time. Although increased interindividual heterogeneity in gene expression was previously reported, whether this process starts during development or if it is restricted to the aging period has not yet been studied. the regulatory dynamics and functional significance of putative aging-related heterogeneity are also unknown. Here we address these by a meta-analysis of 19 transcriptome datasets from three independent studies, covering diverse human brain regions. We observed a significant increase in inter-individual heterogeneity during aging (20 + years) compared to postnatal development (0 to 20 years). Increased heterogeneity during aging was consistent among different brain regions at the gene level and associated with lifespan regulation and neuronal functions. overall, our results show that increased expression heterogeneity is a characteristic of aging human brain, and may influence aging-related changes in brain functions. Aging is a complex process characterized by a gradual decline in maintenance and repair mechanisms, accompanied by an increase in genetic and epigenetic mutations, and oxidative damage to nucleic acids, protein and lipids 1,2. The human brain experiences dramatic structural and functional changes in the course of aging. These include decline in gray matter and white matter volumes 3 , increase in axonal bouton dynamics 4 and reduced synaptic plasticity, all processes that may be associated with decline in cognitive functions 5. Changes during brain aging are suggested to be a result of stochastic processes, unlike changes associated with postnatal neuronal development that are known to be primarily controlled by adaptive regulatory processes 6-8. The molecular mechanisms underlying age-related alteration of regulatory processes and eventually leading to aging-related phenotypes, however, are little understood. Over the past decade, a number of transcriptome studies focusing on age-related changes in human brain gene expression profiles were published 2,9-12. These studies report aging-related differential expression patterns in many functions, including synaptic functions, energy metabolism, inflammation, stress response, and DNA repair. By analyzing age-related change in gene expression profiles in diverse brain regions, we previously showed that for many genes, gene expression changes occur in opposite directions during postnatal development (pre-20 years of age) and aging (post-20 years of age), which may be associated with aging-related phenotypes in healthy brain aging 13. While different brain regions are associated with specific, and often independent, gene expression profiles 9,10,12 , these studies also show that age-related alteration of gene expression profiles during aging is a widespread effect across different brain regions. One of the suggested effects of aging is increased variability between indivi...

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confidence: 99%

Temporal changes in the gene expression heterogeneity during brain development and aging

Işıldak

Somel

Thornton

et al. 2020

Sci Rep

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“…For example, given that regulatory variations play critical roles in many human diseases (47), understanding how genetic variation contributes to increasing gene expression variability will facilitate the identification of disease-related variants. This is especially true when gene expression heterogeneity characterizes traits or diseases such as aging (48)(49)(50) and cancer (51). For many diseases that display a high degree of phenotypic heterogeneity among patients, we may consider that the increased phenotypic variability is due to variability-controlling mutations (such as evSNPs).…”

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confidence: 99%

Epistasis and destabilizing mutations shape gene expression variability in humans via distinct modes of action

Yang

Wang

Yang

et al. 2015

Preprint

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Increasing evidence shows that, like phenotypic mean, phenotypic variance is also genetically determined, but the underlying mechanisms of genetic control over the variance remain obscure. Here, we conducted variance-association mapping analyses to identify expression variability QTLs (evQTLs), i.e., genomic loci associated with gene expression variance, in humans. We discovered that common genetic variations may contribute to increasing gene expression variability via two distinct modes of action—epistasis and destabilization. Specifically, the epistasis model explains a quarter of the identified evQTLs, of which the formation is attributed to the presence of “third-party” eQTLs that influence the level of gene expression in a fraction, rather than the entire set, of sampled individuals. The destabilization model explains the other three-quarters of evQTLs, which tend to be associated with mutations that disrupt the stability of the transcription process of genes. To show the destabilizing effect, we measured discordant gene expression between monozygotic twins, and time-course stability of gene expression in single samples using repetitive qRT-PCR assays. The destabilizing evQTL SNPs were found to be associated with more pronounced expression discordance between twin pairs and less stable gene expression in single samples. Together, our results suggest that common SNPs may work interactively or independently to shape the variability of gene expression in humans. These findings contribute to the understanding of the mechanisms of genetic control over phenotypic variance and may have implications for the development of variability-centered analytic methods for quantitative trait mapping.

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“…Biological factors, including age, sex, and race, are possible cofounding factors and should be matched in typical case–control studies . A large number of age‐associated genes have been reported in either microarray or RNA‐Seq gene expression profiles of the human brain, and sex and race have been suggested to cause bias in gene expression changes . Well‐matched non‐diseased controls will significantly reduce confounding bias caused by these factors.…”

Section: Methodsological Consideration Of Rna‐seq Study Designmentioning

confidence: 99%

RNA sequencing in post‐mortem human brains of neuropsychiatric disorders

Chun

Bendriem

Garamszegi

et al. 2017

Psychiatry Clin Neurosci

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RNA sequencing (RNA-Seq), a revolutionary tool for transcriptome profiling, is becoming increasingly important for neuroscientists in studying the transcriptional landscape of the human brain. Studies using this next-generation sequencing technique have already revealed novel insights into the complexity of neurons in the human brain and pathogenesis of complex neurological diseases. In clinical neuroscience, RNA-Seq provides exciting opportunities for improving diagnosis and treatment of neurological diseases by facilitating the development of pharmacotherapies able to modulate gene expression. Furthermore, integrative whole genome sequencing and transcriptome sequencing can provide additional information for the functional role of mutated genes, prioritization of variants, and intron/exon splicing. This review describes the current state of RNA-Seq studies in neuropsychiatric disorders using post-mortem human brains, a brief survey of best practices for experimental design and sequencing data analysis, and the challenges associated with its application in the human brain.

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Aging Shapes the Population-Mean and -Dispersion of Gene Expression in Human Brains

Cited by 28 publications

References 82 publications

Temporal changes in the gene expression heterogeneity during brain development and aging

Temporal changes in the gene expression heterogeneity during brain development and aging

Epistasis and destabilizing mutations shape gene expression variability in humans via distinct modes of action

RNA sequencing in post‐mortem human brains of neuropsychiatric disorders

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