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Advances in transcriptome analysis of human brain aging

Abstract

Aging is associated with gradual deterioration of physiological and biochemical functions, including cognitive decline. Transcriptome profiling of brain samples from individuals of varying ages has identified the whole-transcriptome changes that underlie age-associated cognitive declines. In this review, we discuss transcriptome-based research on human brain aging performed by using microarray and RNA sequencing analyses. Overall, decreased synaptic function and increased immune function are prevalent in most regions of the aged brain. Age-associated gene expression changes are also cell dependent and region dependent and are affected by genotype. In addition, the transcriptome changes that occur during brain aging include different splicing events, intersample heterogeneity, and altered levels of various types of noncoding RNAs. Establishing transcriptome-based hallmarks of human brain aging will improve the understanding of cognitive aging and neurodegenerative diseases and eventually lead to interventions that delay or prevent brain aging.

Introduction

Aging is a nearly universal biological process associated with gradual deterioration in physiological and biochemical functions, including cognitive decline1,2. Aging is also a major risk factor for many neurodegenerative diseases, including Alzheimer’s disease and Parkinson’s disease2,3. This close relationship between aging and neurodegenerative diseases suggests the existence of common transcriptional and posttranscriptional gene regulation mechanisms in the brain.

Age-associated cognitive declines usually correlate with corresponding age-related structural changes in the brain, such as neuronal loss and synaptic changes1,4. However, the correlation is not universal. For example, changes in gene expression profiles in the brain during aging can precede apparent histopathological degeneration of the brain and appear to underlie age-associated cognitive declines at the molecular level5,6,7,8. Therefore, transcriptome analysis of brain aging offers a systematic molecular approach to understand the causes of neurocognitive aging and neurodegenerative diseases.

The transcriptome is defined as “the complete complement of mRNA molecules generated by a cell or population of cells”9,10. The information of cell populations is stored in the genome; therefore, transcriptome profiling is important for interpreting functional genomic elements11. The objectives of transcriptomics, or the study of the transcriptome, are to categorize all transcripts, to analyze the variants of expressed genes and to quantify the levels of transcripts under different conditions12. The subsequent functional characterization of the genes and transcripts of interest obtained from transcriptome analyses also helps validate their biological relevance.

Transcriptome profiling of brain samples from individuals of varying ages is a useful strategy for studying human brain aging by allowing the detection of overall age-associated transcriptome changes. Although transcriptome profiling alone generates descriptive information, assessment and interpretation of this information allows researchers to effectively design subsequent functional analyses of candidate genes and transcripts11,13.

Because human postmortem tissues are difficult to obtain, progress in transcriptome profiling of human brain aging has been very slow. However, international collaboration for the collection of human brain samples and subsequent generation of various sets of experimental data has paved the way for molecular biological research of human brain aging14,15,16. This collaboration has thus contributed to the comprehensive understanding of aging-related transcriptomic changes in the human brain.

In this article, we review and discuss studies based on transcriptome analyses of human brain aging (Table 1). We categorize the subtopics of transcriptome analysis into global gene expression changes, cell type-specific expression changes, gene coexpression networks, expression quantitative trait loci, and alternative splicing. Decreased synaptic function and increased immune function are prevalent in most analyses, in addition to other features that have been detected in some studies. Increased intersample heterogeneity is also an indispensable factor in the understanding of human brain aging. In addition, we address age-associated expression changes in various types of noncoding RNAs, including microRNAs (miRNAs), long noncoding RNAs (lncRNAs), and circular RNAs (circRNAs). Transcriptome analysis of human brain aging will pave the way for the early diagnosis and prevention of cognitive aging and age-associated brain diseases.

Table 1 Representative transcriptomic studies of human brain aging discussed in this manuscript.

Age-dependent transcriptome changes in the brain include decreased synaptic function and increased immune function

One of the most extensive transcriptome analyses on human brain aging with a large sample size and diverse ethnicity provided insights into global changes across the whole brain, as well as in specific regions17. Generally, temporal regulation of protein-coding genes is more pronounced than spatial or regional regulation (Fig. 1). The expression of 57.7% of genes is changed during prenatal development. In addition, 9.1% of the gene transcripts that are expressed in the neocortex are changed during postnatal development (an age of under 20 years), but only 0.7% of them are altered during adulthood (an age of over 20 years). These data indicate that the majority of differentially expressed genes display temporal changes during development and that the effects of aging on gene expression changes are relatively minor.

Fig. 1: Global age-dependent transcriptomic changes in human brains.
figure1

Expression changes are mainly temporal by relative preservation of their spatial identity. Substantial expression changes are detected in the sixth and seventh decades of human life. The directions of gene expression changes during adult aging tend to be opposite those occurring during fetal development. Throughout multiple brain regions, age-dependent transcriptomic changes include decreased synaptic function and increased immunity. It is worth noting that synaptic aging occurs even prior to neuronal loss. Decreased synaptic function underlies increased expression of repressor element-1-silencing transcription factor (REST) and decreased expression of the tumor protein 73 (TP73) gene. Increased immune responses are accompanied by upregulation of microglial genes and complement component 1q A (C1QA) and downregulation of genes encoding immunosuppressive factors, including C-X3-C motif chemokine receptor 1 (CX3CR1).

However, research using adult brains has indicated noticeable transcriptional changes during adult aging (Fig. 1). Another comprehensive transcriptome study analyzing the prefrontal cortex of 269 human individuals indicates that reduced rates of gene expression changes in the early postnatal brain are maintained throughout early childhood and through the first four decades of life18. Thereafter, substantial gene expression changes across multiple brain regions are detected in the sixth and seventh decades of human life, based on an analysis mostly including people of Caucasian ancestry6,18, indicating the existence of a qualitative transition phase (Fig. 1). The majority of the gene expression changes observed during adult aging are similar to those detected during postnatal development, which includes infancy, childhood, and puberty; interestingly, the directions of these changes are opposite those observed during fetal development18,19 (Fig. 1). In addition, males display a larger number of gene expression changes across multiple brain regions with aging than females6,17. In contrast to this sexual dimorphism, a study that analyzed two large data sets containing over 1800 human samples shows that the expression of genes exhibits similar changes with aging across brain regions20.

Notably, many independent transcriptome analyses have commonly indicated that genes that are upregulated during aging in most brain regions are enriched in inflammatory or immune-responsive genes annotated in the Gene Ontology (GO) database5,6,20,21,22,23,24,25 (Fig. 1). Although appropriate immune activation is neuroprotective, chronic immune activation appears to increase the vulnerability of the brain to neurocognitive aging and neurodegenerative diseases22,23,26. In contrast, multiple studies have repeatedly reported that genes that are downregulated across multiple brain regions during aging are enriched with genes related to synaptic transmission and plasticity5,6,7,21,24,25,27,28 (Fig. 1). These findings suggest that age-associated cognitive decline is driven by synaptic aging without neuronal loss1,5. Consistent with these observations, accumulating evidence indicates that dysregulation of intracellular calcium ion homeostasis is linked to age-related cognitive declines29,30. Genes that exhibit age-dependent expression changes in brain regions are enriched in other biological processes as well; these processes include neurotransmitter transport, energy metabolism, chaperone response, apoptosis, RNA metabolism, amino acid biosynthesis, DNA repair, mitochondrial function, and reactive oxygen species metabolism5,19,21,27,31,32,33. Many of these processes appear to be neuroprotective.

Recent transcriptomic studies in combination with thorough molecular genetic analysis indicate that repressor element-1-silencing transcription factor (REST), a neuronal repressor that is crucial for brain development, functions as a neuroprotective factor against brain aging34. The expression levels of REST are increased in normal aged brains but not in the brains of individuals with mild cognitive impairment or Alzheimer’s disease25,34. REST downregulates excitatory synaptic transmission and upregulates the longevity-associated transcription factor forkhead box O1 (FOXO1) in the mammalian brain, as well as its homolog DAF-16 in Caenorhabditis elegans to promote longevity25. These studies regarding REST suggest the existence of active protective mechanisms in the brain against neurodegeneration and the contribution of reduced excitatory synaptic transmission to longevity (Fig. 1).

Age-dependent gene expression changes in specific cell types have also been detected in diverse brain regions. Multiple papers have commonly reported that neuron-specific genes are predominantly downregulated in all brain regions during aging7,19,21,33,35, but these genes display region-specific, age-dependent expression changes by preserving their regional identity20. In contrast, glia-specific genes with age-dependent alterations in their expression lose their regional identity, and their expression is shifted to multiple regions20. Microglia act as innate immune cells of the central nervous system36. During aging, microglia-specific genes are generally upregulated in all brain regions, with the exception of the cerebellum20,21,23,26 (Fig. 1). In addition, endothelial cell-specific genes are transcriptionally upregulated across multiple brain regions during aging20. Oligodendrocytes act as supporting cells for neurons in the central nervous system by forming myelin sheaths around axons37. In contrast to the expression level changes in microglia-specific and endothelial cell-specific genes, the expression levels of oligodendrocyte-specific genes tend to decrease in various brain regions during aging20,35. These findings provide evidence supporting the occurrence of global and cell- or region-specific transcriptional changes during human brain aging.

Gene coexpression network analysis supports decreased synaptic function and increased immune function during brain aging

Aging is regulated by complex interplay among various components;2,3,8 therefore, the transcriptomes of aged samples usually exhibit high intersample variation. Gene coexpression network analysis is utilized for detecting modules or clusters of highly correlated genes associated with specific functions and for obtaining key information embedded in heterogeneous transcriptomes38 (Fig. 2a). A network analysis of published transcriptomic data obtained from adult hippocampus samples has indicated that a module of genes that display age-dependent upregulation is enriched in myelination39. This study also confirmed an immune process-enriched module that correlated positively with age and a synaptic transmission-enriched module that correlated negatively with age39.

Fig. 2: Targeted analysis of transcriptomic changes during human brain aging.
figure2

a Age-dependent upregulation of myelination-related genes, which is undetectable in global analysis, is detected via gene coexpression network analysis. Genes that exhibit highly positive correlations among themselves are connected to one another. Different colors represent different groups of genes related to specific functions. b Glia-specific genes lose their regional identity during brain aging, whereas neuron-specific genes preserve their regional identity. Glial markers include genes that are specifically expressed in astrocytes and oligodendrocytes. The dimensions of the transcriptome are reduced for visualization by principal component analysis or multidimensional scaling.

Although decreased synaptic function has been reproducibly reported in aged prefrontal cortices40, the increase in immune function in aged brains has been disputed by one study that analyzed three data sets obtained from the frontal cortices of 381 individuals41. Specifically, the expression levels of multiple genes in a microglia-specific module, including microglia surface receptors and Toll-like receptors, decrease in the prefrontal cortex with age41. Thus, some functions of microglia appear to decline during brain aging. However, the microglia module also contains genes encoding immunosuppressive factors, including C-X3-C motif chemokine receptor 1 (CX3CR1);41 therefore, the balance between immune activation and suppression is important for appropriate immune responses6,22. For instance, the microglia-specific neuroprotective receptor for fractalkine (FKN), CX3CR1, suppresses excessive immune responses via microglia activation in mice and rats42,43. Signaling through the FKN ligand and its receptor, CX3CR1, in microglia is reduced in aged brains22,41,43, and this reduction likely contributes to neurotoxic inflammation and results in neurodegeneration (Fig. 1). However, we cannot rule out the possibility that age-dependent downregulation of a subset of microglia-specific genes may reflect the complex regulation of these genes, as well as the altered number of microglia during aging. Collectively, these findings based on experimental data from multiple studies show that decreased synaptic function and increased immune function are predominant during human brain aging.

Glia-specific genes lose their regional identity during brain aging

Examination of the age-dependent changes in the expression of cell-type-specific genes reveals that genes specific to glia, including astrocytes and oligodendrocytes, exhibit altered regional expression patterns during brain aging20 (Fig. 2b). These changes in the regional expression of glia-specific genes is most prominent in the hippocampus in the forebrain and in the substantia nigra in the midbrain, both of which are susceptible to Alzheimer’s disease and Parkinson’s disease pathogenesis. For example, the expression of astrocyte-specific genes in the hippocampus is similar to that observed in the cortex in young brains; however, the expression of these genes shifts toward that observed in intralobular white matter and the putamen in aged brains. Oligodendrocyte-specific genes exhibit differential expression in the hippocampus and in the substantia nigra in young brains; however, their expression in these structures becomes similar in aged brains. In contrast, neuron-specific genes preserve their regional identity during brain aging (Fig. 2b). Thus, during human brain aging, the expression of genes in specific cell types, including astrocytes and oligodendrocytes, changes dramatically and regional identities are lost.

Analysis of expression quantitative trait loci (eQTLs) reveals that genetic backgrounds affect gene expression in an age-dependent manner

Quantitative trait locus (QTL) analysis is a method that is used to infer the effects of genetic background on different traits. Expression QTLs (eQTLs) are genomic loci that are highly related to the expression levels of target genes44,45. Many eQTLs show consistent effects on the expression levels of their targets independent of age17,18. Interestingly, however, several studies using less stringent thresholds or a particular type of cell, such as microglia, have reported that some of these loci display differential effects on the brain at different ages26,32. One example is the single-nucleotide polymorphism (SNP) rs5567529832; individuals with the GG allele show an age-dependent increase in the expression of the tumor protein 73 (TP73) gene, which encodes a member of the p53 protein family, whereas individuals with the GT or TT alleles do not exhibit this increase (Fig. 3a). TP73 is critical for normal neuronal development and survival in mice46,47,48; therefore, age-dependent changes in the expression of TP73 potentially affect brain aging and age-related diseases (Fig. 1). Indeed, the allele frequency of TP73 is implicated in human Alzheimer’s disease49, and haploinsufficiency of TP73 confers susceptibility to Alzheimer’s disease in model mice50. Similarly, the GG allele of the SNP rs72788737 is associated with age-dependent increases in the expression of complement component 1q A (C1QA), which encodes the C1q protein responsible for innate immune responses, whereas the GT or TT allele is not32. The expression levels of C1QA increase during normal brain aging (Fig. 1), and the C1q protein accumulates in several brain regions, including the hippocampus, substantia nigra, and piriform cortex, which are vulnerable to neurodegeneration51. In addition, the number of neuroprotective apolipoprotein E (APOE) gene ε2 haplotypes but not APOE ε4 haplotypes is associated with reduced expression levels of microglia-enriched genes in aged brains26. This pattern suggests that the APOE ε2 haplotypes moderate the excessive microglial activation that occurs during aging. Overall, these studies show the interaction between the genetic background and age-dependent gene expression changes, which may in turn affect human brain aging and age-associated diseases.

Fig. 3: Genetic backgrounds that affect gene expression and alternative splicing in an age-dependent manner.
figure3

a. An example of expression quantitative trait loci (eQTLs) during brain aging. Genotypes of eQTL single-nucleotide polymorphisms (SNPs) are associated with the expression of target genes (eGenes) on the same chromosome (cis-eQTLs) or on different chromosomes (trans-eQTLs). Individuals with the GG allele in the SNP rs55675298 exhibit an age-dependent increase in the expression of the tumor protein-coding p53 family gene TP73, whereas individuals with the GT or TT allele do not. b. An example of a splicing quantitative trait locus (sQTL) in aged brains. Genotypes of sQTL SNPs are associated with alternative splicing of target genes. The number of G alleles in the SNP rs2439540 correlates positively with hnRNP splicing factors, including HNRNPA2B1, and correlates negatively with the intron usage level of TBC1 domain family member 7 (TBC1D7). Consistent with these observations, the mRNA level of HNRNPA2B1 correlates negatively with the intron usage level of TBC1D7.

Analysis of alternative splicing variants suggests the role of RNA-binding proteins in brain aging

In addition to gene expression changes, two notable papers that analyzed splicing changes have indicated that human brain aging is associated with alternative splicing17,52. Variations in alternative splicing are predominant during prenatal development17. After birth, 0.9% of the genes expressed in the neocortex exhibit differential splicing during postnatal development, whereas 1.4% of them do so during adulthood17. As the brain exhibits the highest level of splicing and preferential expression of genes encoding RNA-binding proteins among all organs of the body16, the 1.4% rate of age-dependent splicing changes is substantial compared with the 0.9% rate.

Remarkably, age-dependent splicing changes do not correlate with changes in the expression of the corresponding genes52. The splicing changes that occur during postnatal development and during subsequent aging processes display opposite trends;52 75% of segments (exons or introns) show decreased inclusion in transcripts during postnatal development, whereas 77% of segments show increased inclusion during aging. The majority of segments that follow this “down–up” inclusion pattern are retained introns52. Transcripts with retained introns can be targets for nonsense-mediated decay (NMD), which is a crucial system for RNA quality control that degrades various mRNAs, including those with premature stop codons, and regulates alternative splicing53. Among the 15 of the 21 genes encoding NMD pathway components that are expressed in the brain, five [up-frameshift suppressor 3 homolog B (UPF3b), suppressor with morphological effect on genitalia homolog 1 (SMG1), SMG5, SMG6, and cancer susceptibility candidate 3 (CASC3)] follow the “down–up” pattern52. These findings suggest that components acting in the NMD pathway display characteristic age-dependent expression changes. NMD plays crucial roles in organismal longevity in invertebrates54,55. Thus, it will be interesting to evaluate whether the age-dependent expression changes in NMD pathway component-encoding genes contribute to brain aging or age-associated neurodegenerative diseases.

The expression levels of six genes encoding splicing factors, i.e., polypyrimidine tract-binding proteins 1 and 2 (PTBP1 and PTBP2) and heterogeneous nuclear ribonucleoproteins A1, H1, H3, and F (hnRNPA1, hnRNPH1, hnRNPH3, and hnRNPF), follow the age-dependent “down–up” expression pattern52. The expression levels of these genes correlate positively with the “down–up” splicing pattern of particular genes and correlate negatively with the “up–up” or “up–down” splicing patterns of other genes. These relationships may reflect the idea that PTBP1 and PTBP2, which are located in the vicinity of spliced segments56, positively or negatively affect segment inclusion57. Indeed, a large proportion of PTB targets are segments that exhibit age-related splicing changes35.

Analysis of splicing QTLs (sQTLs), also called splicing-isoform QTLs (isoQTLs), identified 9,044 genomic loci associated with splicing events in 3,006 genes in the aged prefrontal cortex, including the SNP rs243954058 (Fig. 3b). Although most of these sQTLs do not appear to affect gene expression, 42 loci correlate with the expression and splicing of the genes that contain the loci58. In addition, the 9,044 age-associated sQTLs are enriched with potential binding of 18 RNA-binding proteins (RBPs);58 PTBP1 is the most prevalently bound RBP, followed by HNRNPC, cleavage and polyadenylation specific factor 7 (CPSF7), and ELAVL1 [embryonic lethal, abnormal vision (ELAV)-like RNA-binding protein 1]. The age-associated sQTLs are also enriched with other hnRNPs58. For example, the frequency of the G allele of the SNP rs2439540, which correlates negatively with intron usage in the TBC1 domain family member 7 (TBC1D7) gene, correlates positively with binding of HNRNPA2B1 (Fig. 3b). Moreover, the mRNA levels of hnRNP splicing factors, including HNRNPA2B1 and HNRNPC, correlate negatively with the intron usage levels of hundreds of genes (Fig. 3b), some of which are associated with Alzheimer’s disease58. These findings suggest that these RBPs, as well as PTBP1 and PTBP2, play roles in the age-dependent changes in splicing.

The increased variability of gene expression indicates impairment of genome maintenance and repair with age

As described above, the transcriptome of the aged brain after the sixth and seventh decades of human life becomes similar to that of the fetal brain18,19. However, these changes are not consistently detected between cells or tissues, because the gene expression profiles of brains become heterogeneous during aging5,27. This increased variability of gene expression appears to be caused by accumulation of cellular damage and somatic mutations rather than by differences in germline mutations or aging rates between individuals2,59,60,61. Indeed, a pioneering paper that analyzed microarray data obtained from humans of various ages reported that DNA damage is highly enriched in the promoters of genes that are downregulated in aged brains5. This finding is consistent with reports showing that gene expression variability is more pronounced among age-dependently downregulated genes than age-dependently upregulated genes33,60. The expression of many age-dependently downregulated genes correlates with increased DNA damage5, indicating accelerated genome instability during brain aging. The susceptibility of specific brain regions, such as the hippocampus, substantia nigra, and basal ganglia, to DNA damage may be involved in the increased vulnerability of these brain regions to aging compared with that of other regions62. Prolonged accumulation of cellular defects often results in immune activation, which causes additional damage63, consistent with the increased immune responses observed in aged brains. In summary, age-dependent downregulation of genes associated with increased genome instability may underlie the accumulation of stochastic events in aged brain cells. Old cells subsequently become heterogeneous and inflammatory, and these changes may eventually lead to gradual declines in the physiological integrity of the brain.

Noncoding RNAs that participate in human brain aging

Transcriptomic studies have identified many novel classes of noncoding RNAs (ncRNAs) that are crucial for brain physiology64,65. Moreover, functional analyses have established ncRNAs as key regulators of brain aging31,66,67. In this section, we highlight the major classes of ncRNAs, i.e., microRNAs (miRNAs), long noncoding RNAs (lncRNAs), and circular RNAs (circRNAs), that participate in human brain aging (Fig. 4).

Fig. 4: Noncoding RNAs that participate in human brain aging.
figure4

a Several microRNAs (miRNAs) enhance neuroprotection in the elderly at the cost of inflammation. The expression of miR-29a/b, miR-34a, and miR-144 increases with age, whereas that of miR-222 decreases. b Long noncoding RNAs (lncRNAs) display age-dependent expression changes with preserved regional specificity. In the prefrontal cortex, the expression levels of the antisense RNA of the gene encoding proline-rich protein (PRR34-AS1), LINC01094, and LINC00844 correlate positively with age. In contrast, the expression levels of the antisense RNA of the gene encoding opacity‐associated (Opa) interacting protein 1 (OIP5-AS1), MIR7-3 host gene (MIR7-3HG), LINC00643, LINC00507, and brain cytoplasmic 200 (BC200) correlate negatively with age. In the subependymal zone, GOMAFU, metastasis-associated lung adenocarcinoma transcript 1 (MALAT1), nuclear paraspeckle assembly transcript 1 (NEAT1), and taurine-upregulated gene 1 (TUG1) display age-dependent upregulation, whereas the expression of LINC00657 and small nucleolar RNA, C/D box 3A (SNORD3A) is downregulated with age. c Circular RNAs (circRNAs) tend to accumulate during brain aging, but the function of their accumulation in aging remains unclear.

Several miRNAs enhance adaptive neuroprotection in the elderly at the cost of microglial inflammation

Two independent studies that reported in-depth analysis of miRNAs indicated that expression changes in miRNAs are prominent during human brain aging19,68. Overall, the expression of miRNAs in human brains decreases with age after adolescence; in contrast, a subset of miRNAs display age-dependent upregulation19,68. In some cases, age-dependent expression changes in miRNAs appear to be protective against neurodegenerative diseases and brain aging. The expression of miR-144 increases with age in the cerebellum and suppresses the expression of the ataxin 1 (ATXN1) gene69 (Fig. 4a). Because trinucleotide repeat expansion mutations in ATXN1, which augment the glutamine repeats in the encoded protein, underlie the development of spinocerebellar ataxia 1 (SCA1)70, miR-144 may play a protective role against this neurodegenerative disease. In addition, an age-dependent decrease in miR-222 expression appears to result in derepression of its target genes19, including REV1/deoxycytidyl transferase, which regulates the DNA damage response71 (Fig. 4a). This process may eventually reduce the excessive DNA damage in aged brains.

However, the adaptive neuroprotection afforded by age-dependent expression changes in several miRNAs also seems to be associated with health complications, including excessive microglial inflammation31. For example, miR-34a, which acts as a tumor suppressor in neuroblastoma72, exhibits an age-dependent increase, which in turn downregulates neuronal genes, thus possibly participating in brain aging19 (Fig. 4a). In addition, the expression of miR-29a/b is dramatically increased with age in the superior frontal gyrus and the postcentral parietal area19,73 and exerts neuroprotective functions74,75, as well as induces excessive microglial inflammation73 (Fig. 4a). Low expression of miR-29a/b is observed in the brains of patients with sporadic Alzheimer’s disease and correlates with high BACE1/β-secretase levels74 and a high amyloid beta load76, again suggesting the neuroprotective role of miR-29a/b. Conversely, miR-29a/b expression correlates negatively with the mRNA levels of insulin-like growth factor 1 (IGF1) and CX3CR173, both of which inhibit excessive microglial activation42,43,77. Thus, the age-dependent increases in miR-29a/b levels seem to protect against neurodegeneration in the elderly at the cost of excessive microglial inflammation (Fig. 4a).

lncRNAs display high regional specificity in aging human brains

Multiple studies focusing on analysis of lncRNAs have reported that aged human brains exhibit expression changes in various lncRNAs with regional specificity78,79,80,81 (Fig. 4b). Among the 383 lncRNAs that are differentially expressed between young and aged samples from four brain regions, only eight exhibit consistent changes in all brain regions80, indicating the high regional specificity of lncRNAs.

In the prefrontal cortex, the expression levels of the antisense RNA of the gene encoding proline-rich protein (PRR34-AS1), LINC01094, and LINC00844 correlate positively with age, whereas the expression levels of the antisense RNA of the gene encoding opacity‐associated (Opa) interacting protein 1 (OIP5-AS1), MIR7-3 host gene (MIR7-3HG), LINC00643, LINC00507, and brain cytoplasmic 200 (BC200) correlate negatively with age78,81 (Fig. 4b). These age-dependent changes in the expression of lncRNAs in the prefrontal cortex reflect cognitive declines with age. LINC00507, which has ribosome-binding activity and micropeptide-coding potential, is specific to cortical regions in primates82, suggesting that its downregulation with age is associated with cognitive decline. Another lncRNA, BC200, inhibits local mRNA translation in dendrites and synapses83,84. The expression level of BC200 in the prefrontal cortex decreases by 65% between the ages of 49 and 86 years78 and correlates with age-dependent declines in synaptic functions85.

In the subependymal zone of the adult brain, which contains neural stem cells86, GOMAFU [also called myocardial infarction associated transcript (MIAT) and retinal noncoding RNA 2 (RNCR2)], metastasis-associated lung adenocarcinoma transcript 1 (MALAT1), nuclear paraspeckle assembly transcript 1 (NEAT1), and taurine-upregulated gene 1 (TUG1) exhibit age-dependent upregulation, whereas the expression of LINC00657 and small nucleolar RNA, C/D box 3A (SNORD3A) is downregulated with age79 (Fig. 4b). GOMAFU acts as a scaffold for the sequestration of splicing factors and modulates the splicing of transcripts that are required for synaptic plasticity87. Neuronal activation decreases the expression of GOMAFU and releases splicing factors into the nucleoplasm87. In addition to GOMAFU, MALAT1 and NEAT1 are located in nuclear speckles and paraspeckles, respectively, where they regulate alternative splicing in neurons by sequestering splicing factors88. However, the mechanisms by which MALAT1 and NEAT1 participate in brain aging remain poorly understood. TUG1 sequesters miR-9, and its upregulation leads to release of BCL2 like 11 (BCL2L11) mRNA from miR-9 to promote apoptosis in rats with ischemia89. Thus, the age-dependent increase in the expression of TUG1 in the subependymal zone may indicate the acceleration of neuronal apoptosis in aged brains79.

The age-dependent expression changes in a subset of lncRNAs also display specificity for other brain regions. The postcentral gyrus and superior frontal gyrus exhibit a larger number of differentially expressed lncRNAs than does the entorhinal cortex or hippocampus80. The majority of the age-dependent changes in lncRNA expression manifest as upregulation in all regions except the entorhinal cortex80. Overall, lncRNAs appear to be more region-specific than mRNAs in aging human brains.

circRNA levels tend to increase during human brain aging

circRNAs are mostly noncoding RNAs that are formed by backsplicing events;90 however, the biological functions of circRNAs remain largely unexplored. Unlike linear RNAs, circRNAs are covalently linked at their termini and lack 3′ poly(A) tails; therefore, circRNAs display high resistance against exonucleases91,92,93. This property endows circRNAs with enhanced stability and raises the possibility that the levels of circRNAs remain high in nonproliferating cells. The levels of circRNAs do not correlate positively with those of their cognate mRNA isoforms94,95. circRNAs are highly expressed in brain tissues compared with nonbrain tissues in mice and humans94. Importantly, circRNAs display age-dependent accumulation in the brains of mice and a nonhuman primate, the rhesus macaque95,96 (Fig. 4c). In addition, certain circRNAs are specifically detected in patients with Alzheimer’s disease97. Therefore, although circRNAs have not been directly characterized in the aging human brain, it seems likely that the levels of circRNAs increase during human brain aging. It will be important to determine by future research whether the accumulation of circRNAs plays a role in aging and age-related diseases.

Conclusions and perspectives

Transcriptome analysis of human brain aging offers a comprehensive view of neurocognitive aging and neurodegenerative diseases. Here, we reviewed studies regarding the transcriptome changes associated with human brain aging. Decreased synaptic function and increased immune function are prevalent in most brain regions during aging, but other age-associated transcriptome changes display cellular or regional specificities. Gene expression in glial cells, including astrocytes and oligodendrocytes, is dramatically altered during brain aging and is accompanied by loss of the regional identities of these genes. Age-dependent gene expression changes are also affected by genetic backgrounds and splicing events. The role of RBPs, including splicing factors, is important for the splicing events in many transcripts during brain aging. Increased intersample heterogeneity is a property that implies the impairment of genome maintenance and repair in aged brains. The role of age-associated expression changes in noncoding RNAs, including miRNAs, lncRNAs, and circRNAs, remains less well understood than those in mRNAs. Thus, future studies of these noncoding RNAs will lead to a deeper understanding of brain aging at the molecular level.

The emergence of single-cell RNA-seq technology has enhanced the potential of transcriptome analysis and will help improve the understanding of human brain aging at the single-cell level. Specifically, investigation of single-cell transcriptomes provides unbiased insights into the subtypes and the diversities of brain cells98,99. Single-cell RNA-seq overcomes the problem of bulk RNA-seq, i.e., that the transcriptional contribution from minor cell types is masked by major cell types in tissues. Currently, examination of single nuclei is usually preferred98. However, this method cannot estimate the expression of cytosolic transcripts. Therefore, optimization of single-cell isolation is required for brain transcriptome analysis at the single-cell level. A recent study reported successful analysis of the single-cell transcriptome in the aging mouse brain by introducing sophisticated brain tissue dissociation steps100. Thus, future research employing single-cell RNA-seq technology with human brain samples will pave the way for providing new insights into human brain aging. In addition, by combining this single-cell technique with in situ hybridization101, transcriptome analysis will allow spatial separation for visualizing gene expression in human brain cells during aging.

One of the limitations of previous research on transcriptome analysis of the human brain is that the majority of participants in postmortem brain studies are of Caucasian ethnicity. Among 1007 individuals with specified ethnicity in previous studies (Table 1), 733 (72.8%) and 32 (3.2%) were Caucasian and European, respectively. African-American (198, 19.7%) and African (16, 1.6%), Hispanic (18, 1.8%) and Asian (8, 0.8%) populations comprised a minor fraction. Thus, our understanding of human brain aging may be effective for only a subset of the human population at best. Future investigation of transcriptomes obtained from a wide spectrum of ethnic groups will be crucial for a comprehensive understanding of human brain aging.

In conclusion, understanding the transcriptome in aged human brains will lead to the early detection of cognitive aging and the development of interventions that delay brain aging at the molecular level. Furthermore, given the close relationship between brain aging and neurodegenerative diseases, the establishment of transcriptome-based hallmarks of human brain aging will facilitate the diagnosis and treatment of these disorders.

References

  1. 1.

    Morrison, J. H. & Baxter, M. G. The ageing cortical synapse: hallmarks and implications for cognitive decline. Nat. Rev. Neurosci. 13, 240–250 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  2. 2.

    Lopez-Otin, C., Blasco, M. A., Partridge, L., Serrano, M. & Kroemer, G. The hallmarks of aging. Cell 153, 1194–1217 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  3. 3.

    Kennedy, B. K. et al. Geroscience: linking aging to chronic disease. Cell 159, 709–713 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  4. 4.

    Pakkenberg, B. et al. Aging and the human neocortex. Exp. Gerontol. 38, 95–99 (2003).

    PubMed  Google Scholar 

  5. 5.

    Lu, T. et al. Gene regulation and DNA damage in the ageing human brain. Nature 429, 883–891 (2004).

    CAS  PubMed  Google Scholar 

  6. 6.

    Berchtold, N. C. et al. Gene expression changes in the course of normal brain aging are sexually dimorphic. Proc. Natl Acad. Sci. USA 105, 15605–15610 (2008).

    CAS  PubMed  Google Scholar 

  7. 7.

    Loerch, P. M. et al. Evolution of the aging brain transcriptome and synaptic regulation. PLoS ONE 3, e3329 (2008).

    PubMed  PubMed Central  Google Scholar 

  8. 8.

    Khan, S. S., Singer, B. D. & Vaughan, D. E. Molecular and physiological manifestations and measurement of aging in humans. Aging Cell 16, 624–633 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. 9.

    Velculescu, V. E. et al. Characterization of the yeast transcriptome. Cell 88, 243–251 (1997).

    CAS  PubMed  Google Scholar 

  10. 10.

    Pietu, G. et al. The Genexpress IMAGE knowledge base of the human brain transcriptome: a prototype integrated resource for functional and computational genomics. Genome Res. 9, 195–209 (1999).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. 11.

    Lowe, R., Shirley, N., Bleackley, M., Dolan, S. & Shafee, T. Transcriptomics technologies. PLoS Comput Biol. 13, e1005457 (2017).

    PubMed  PubMed Central  Google Scholar 

  12. 12.

    Wang, Z., Gerstein, M. & Snyder, M. RNA-Seq: a revolutionary tool for transcriptomics. Nat. Rev. Genet. 10, 57–63 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. 13.

    Wolf, J. B. Principles of transcriptome analysis and gene expression quantification: an RNA-seq tutorial. Mol. Ecol. Resour. 13, 559–572 (2013).

    CAS  PubMed  Google Scholar 

  14. 14.

    Hawrylycz, M. J. et al. An anatomically comprehensive atlas of the adult human brain transcriptome. Nature 489, 391–399 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. 15.

    Hawrylycz, M. et al. Canonical genetic signatures of the adult human brain. Nat. Neurosci. 18, 1832–1844 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. 16.

    Mele, M. et al. The human transcriptome across tissues and individuals. Science 348, 660–665 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. 17.

    Kang, H. J. et al. Spatio-temporal transcriptome of the human brain. Nature 478, 483–489 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. 18.

    Colantuoni, C. et al. Temporal dynamics and genetic control of transcription in the human prefrontal cortex. Nature 478, 519–523 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. 19.

    Somel, M. et al. MicroRNA, mRNA, and protein expression link development and aging in human and macaque brain. Genome Res. 20, 1207–1218 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. 20.

    Soreq, L. et al. Major shifts in glial regional identity are a transcriptional hallmark of human brain aging. Cell Rep. 18, 557–570 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. 21.

    Erraji-Benchekroun, L. et al. Molecular aging in human prefrontal cortex is selective and continuous throughout adult life. Biol. Psychiatry 57, 549–558 (2005).

    CAS  PubMed  Google Scholar 

  22. 22.

    Cribbs, D. H. et al. Extensive innate immune gene activation accompanies brain aging, increasing vulnerability to cognitive decline and neurodegeneration: a microarray study. J. Neuroinflammation 9, 179 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. 23.

    Norden, D. M. & Godbout, J. P. Review: microglia of the aged brain: primed to be activated and resistant to regulation. Neuropathol. Appl. Neurobiol. 39, 19–34 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. 24.

    Primiani, C. T. et al. Coordinated gene expression of neuroinflammatory and cell signaling markers in dorsolateral prefrontal cortex during human brain development and aging. PLoS ONE 9, e110972 (2014).

    PubMed  PubMed Central  Google Scholar 

  25. 25.

    Zullo, J. M. et al. Regulation of lifespan by neural excitation and REST. Nature 574, 359–364 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. 26.

    Olah, M. et al. A transcriptomic atlas of aged human microglia. Nat. Commun. 9, 539 (2018).

    PubMed  PubMed Central  Google Scholar 

  27. 27.

    Naumova, O. Y. et al. Age-related changes of gene expression in the neocortex: preliminary data on RNA-Seq of the transcriptome in three functionally distinct cortical areas. Dev. Psychopathol. 24, 1427–1442 (2012).

    PubMed  PubMed Central  Google Scholar 

  28. 28.

    Berchtold, N. C. et al. Synaptic genes are extensively downregulated across multiple brain regions in normal human aging and Alzheimer’s disease. Neurobiol. Aging 34, 1653–1661 (2013).

    CAS  PubMed  Google Scholar 

  29. 29.

    Riascos, D. et al. Alterations of Ca2+-responsive proteins within cholinergic neurons in aging and Alzheimer’s disease. Neurobiol. Aging 35, 1325–1333 (2014).

    CAS  PubMed  Google Scholar 

  30. 30.

    Rice, R. A., Berchtold, N. C., Cotman, C. W. & Green, K. N. Age-related downregulation of the CaV3.1 T-type calcium channel as a mediator of amyloid beta production. Neurobiol. Aging 35, 1002–1011 (2014).

    CAS  PubMed  Google Scholar 

  31. 31.

    Mohan, A., Mather, K. A., Thalamuthu, A., Baune, B. T. & Sachdev, P. S. Gene expression in the aging human brain: an overview. Curr. Opin. Psychiatry 29, 159–167 (2016).

    PubMed  Google Scholar 

  32. 32.

    Brinkmeyer-Langford, C. L., Guan, J., Ji, G. & Cai, J. J. Aging shapes the population-mean and -dispersion of gene expression in human brains. Front. Aging Neurosci. 8, 183 (2016).

    PubMed  PubMed Central  Google Scholar 

  33. 33.

    Donertas, H. M. et al. Gene expression reversal toward pre-adult levels in the aging human brain and age-related loss of cellular identity. Sci. Rep. 7, 5894 (2017).

    PubMed  PubMed Central  Google Scholar 

  34. 34.

    Lu, T. et al. REST and stress resistance in ageing and Alzheimer’s disease. Nature 507, 448–454 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. 35.

    Tollervey, J. R. et al. Analysis of alternative splicing associated with aging and neurodegeneration in the human brain. Genome Res. 21, 1572–1582 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. 36.

    Li, Q. & Barres, B. A. Microglia and macrophages in brain homeostasis and disease. Nat. Rev. Immunol. 18, 225–242 (2018).

    CAS  PubMed  Google Scholar 

  37. 37.

    Kuhn, S., Gritti, L., Crooks, D. & Dombrowski, Y. Oligodendrocytes in development, myelin generation and beyond. Cells 8, 1424 (2019).

    CAS  PubMed Central  Google Scholar 

  38. 38.

    Mitra, K., Carvunis, A. R., Ramesh, S. K. & Ideker, T. Integrative approaches for finding modular structure in biological networks. Nat. Rev. Genet. 14, 719–732 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. 39.

    Bae, S. H. et al. Decipher reliable biomarkers of brain aging by integrating literature-based evidence with interactome data. Exp. Mol. Med. 50, 28 (2018).

    PubMed  PubMed Central  Google Scholar 

  40. 40.

    Dillman, A. A. et al. Transcriptomic profiling of the human brain reveals that altered synaptic gene expression is associated with chronological aging. Sci. Rep. 7, 16890 (2017).

    PubMed  PubMed Central  Google Scholar 

  41. 41.

    Wehrspaun, C. C., Haerty, W. & Ponting, C. P. Microglia recapitulate a hematopoietic master regulator network in the aging human frontal cortex. Neurobiol. Aging 36, 2443 e9–2443. e20 (2015).

  42. 42.

    Cardona, A. E. et al. Control of microglial neurotoxicity by the fractalkine receptor. Nat. Neurosci. 9, 917–924 (2006).

    CAS  PubMed  Google Scholar 

  43. 43.

    Bachstetter, A. D. et al. Fractalkine and CX3CR1 regulate hippocampal neurogenesis in adult and aged rats. Neurobiol. Aging 32, 2030–2044 (2011).

    CAS  PubMed  Google Scholar 

  44. 44.

    Morley, M. et al. Genetic analysis of genome-wide variation in human gene expression. Nature 430, 743–747 (2004).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. 45.

    Grundberg, E. et al. Mapping cis- and trans-regulatory effects across multiple tissues in twins. Nat. Genet. 44, 1084–1089 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. 46.

    Yang, A. et al. p73-deficient mice have neurological, pheromonal and inflammatory defects but lack spontaneous tumours. Nature 404, 99–103 (2000).

    CAS  PubMed  Google Scholar 

  47. 47.

    Pozniak, C. D. et al. An anti-apoptotic role for the p53 family member, p73, during developmental neuron death. Science 289, 304–306 (2000).

    CAS  PubMed  Google Scholar 

  48. 48.

    Pozniak, C. D. et al. p73 is required for survival and maintenance of CNS neurons. J. Neurosci. 22, 9800–9809 (2002).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. 49.

    Li, Q. et al. TP73 allelic expression in human brain and allele frequencies in Alzheimer’s disease. BMC Med. Genet. 5, 14 (2004).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. 50.

    Wetzel, M. K. et al. p73 regulates neurodegeneration and phospho-tau accumulation during aging and Alzheimer’s disease. Neuron 59, 708–721 (2008).

    CAS  PubMed  Google Scholar 

  51. 51.

    Stephan, A. H. et al. A dramatic increase of C1q protein in the CNS during normal aging. J. Neurosci. 33, 13460–13474 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. 52.

    Mazin, P. et al. Widespread splicing changes in human brain development and aging. Mol. Syst. Biol. 9, 633 (2013).

    PubMed  PubMed Central  Google Scholar 

  53. 53.

    Kim, Y. K. & Maquat, L. E. UPFront and center in RNA decay: UPF1 in nonsense-mediated mRNA decay and beyond. RNA 25, 407–422 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. 54.

    Son, H. G. et al. RNA surveillance via nonsense-mediated mRNA decay is crucial for longevity in daf-2/insulin/IGF-1 mutant C. elegans. Nat. Commun. 8, 14749 (2017).

    PubMed  PubMed Central  Google Scholar 

  55. 55.

    Tabrez, S. S., Sharma, R. D., Jain, V., Siddiqui, A. A. & Mukhopadhyay, A. Differential alternative splicing coupled to nonsense-mediated decay of mRNA ensures dietary restriction-induced longevity. Nat. Commun. 8, 306 (2017).

    PubMed  PubMed Central  Google Scholar 

  56. 56.

    Gooding, C., Roberts, G. C. & Smith, C. W. Role of an inhibitory pyrimidine element and polypyrimidine tract binding protein in repression of a regulated alpha-tropomyosin exon. RNA 4, 85–100 (1998).

    CAS  PubMed  PubMed Central  Google Scholar 

  57. 57.

    Llorian, M. et al. Position-dependent alternative splicing activity revealed by global profiling of alternative splicing events regulated by PTB. Nat. Struct. Mol. Biol. 17, 1114–1123 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  58. 58.

    Raj, T. et al. Integrative transcriptome analyses of the aging brain implicate altered splicing in Alzheimer’s disease susceptibility. Nat. Genet. 50, 1584–1592 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  59. 59.

    Kirkwood, T. B. Understanding the odd science of aging. Cell 120, 437–447 (2005).

    CAS  PubMed  Google Scholar 

  60. 60.

    Somel, M., Khaitovich, P., Bahn, S., Paabo, S. & Lachmann, M. Gene expression becomes heterogeneous with age. Curr. Biol. 16, R359–R360 (2006).

    CAS  PubMed  Google Scholar 

  61. 61.

    Kowald, A. & Kirkwood, T. B. L. Can aging be programmed? A critical literature review. Aging Cell 15, 986–998 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  62. 62.

    Fu, H., Hardy, J. & Duff, K. E. Selective vulnerability in neurodegenerative diseases. Nat. Neurosci. 21, 1350–1358 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  63. 63.

    Ioannidou, A., Goulielmaki, E. & Garinis, G. A. DNA damage: from chronic inflammation to age-related deterioration. Front. Genet. 7, 187 (2016).

    PubMed  PubMed Central  Google Scholar 

  64. 64.

    Qureshi, I. A. & Mehler, M. F. Emerging roles of non-coding RNAs in brain evolution, development, plasticity and disease. Nat. Rev. Neurosci. 13, 528–541 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  65. 65.

    Salta, E. & De Strooper, B. Noncoding RNAs in neurodegeneration. Nat. Rev. Neurosci. 18, 627–640 (2017).

    CAS  PubMed  Google Scholar 

  66. 66.

    Earls, L. R., Westmoreland, J. J. & Zakharenko, S. S. Non-coding RNA regulation of synaptic plasticity and memory: implications for aging. Ageing Res Rev. 17, 34–42 (2014).

    CAS  PubMed  Google Scholar 

  67. 67.

    Abraham, K. J., Ostrowski, L. A. & Mekhail, K. Non-coding RNA molecules connect calorie restriction and lifespan. J. Mol. Biol. 429, 3196–3214 (2017).

    CAS  PubMed  Google Scholar 

  68. 68.

    Beveridge, N. J. et al. Maturation of the human dorsolateral prefrontal cortex coincides with a dynamic shift in microRNA expression. Schizophr. Bull. 40, 399–409 (2014).

    PubMed  Google Scholar 

  69. 69.

    Persengiev, S., Kondova, I., Otting, N., Koeppen, A. H. & Bontrop, R. E. Genome-wide analysis of miRNA expression reveals a potential role for miR-144 in brain aging and spinocerebellar ataxia pathogenesis. Neurobiol. Aging 32, 2316 e17–2316 e27 (2011).

  70. 70.

    Orr, H. T. et al. Expansion of an unstable trinucleotide CAG repeat in spinocerebellar ataxia type 1. Nat. Genet. 4, 221–226 (1993).

    CAS  PubMed  Google Scholar 

  71. 71.

    Lin, W. et al. The human REV1 gene codes for a DNA template-dependent dCMP transferase. Nucleic Acids Res. 27, 4468–4475 (1999).

    CAS  PubMed  PubMed Central  Google Scholar 

  72. 72.

    Cole, K. A. et al. A functional screen identifies miR-34a as a candidate neuroblastoma tumor suppressor gene. Mol. Cancer Res. 6, 735–742 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  73. 73.

    Fenn, A. M. et al. Increased micro-RNA 29b in the aged brain correlates with the reduction of insulin-like growth factor-1 and fractalkine ligand. Neurobiol. Aging 34, 2748–2758 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  74. 74.

    Hebert, S. S. et al. Loss of microRNA cluster miR-29a/b-1 in sporadic Alzheimer’s disease correlates with increased BACE1/beta-secretase expression. Proc. Natl Acad. Sci. USA 105, 6415–6420 (2008).

    CAS  PubMed  Google Scholar 

  75. 75.

    Ouyang, Y. B. et al. Astrocyte-enriched miR-29a targets PUMA and reduces neuronal vulnerability to forebrain ischemia. Glia 61, 1784–1794 (2013).

    PubMed  Google Scholar 

  76. 76.

    Li, R. et al. Amyloid beta peptide load is correlated with increased beta-secretase activity in sporadic Alzheimer’s disease patients. Proc. Natl Acad. Sci. USA 101, 3632–3637 (2004).

    CAS  PubMed  Google Scholar 

  77. 77.

    O’Connor, J. C. et al. Regulation of IGF-I function by proinflammatory cytokines: at the interface of immunology and endocrinology. Cell Immunol. 252, 91–110 (2008).

    PubMed  PubMed Central  Google Scholar 

  78. 78.

    Mus, E., Hof, P. R. & Tiedge, H. Dendritic BC200 RNA in aging and in Alzheimer’s disease. Proc. Natl Acad. Sci. USA 104, 10679–10684 (2007).

    CAS  PubMed  Google Scholar 

  79. 79.

    Barry, G., Guennewig, B., Fung, S., Kaczorowski, D. & Weickert, C. S. Long non-coding rna expression during aging in the human subependymal zone. Front. Neurol. 6, 45 (2015).

    PubMed  PubMed Central  Google Scholar 

  80. 80.

    Zhou, M., Zhao, H., Wang, X., Sun, J. & Su, J. Analysis of long noncoding RNAs highlights region-specific altered expression patterns and diagnostic roles in Alzheimer’s disease. Brief. Bioinform. 20, 598–608 (2019).

    CAS  PubMed  Google Scholar 

  81. 81.

    Cao, M., Li, H., Zhao, J., Cui, J. & Hu, G. Identification of age- and gender-associated long noncoding RNAs in the human brain with Alzheimer’s disease. Neurobiol. Aging 81, 116–126 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  82. 82.

    Mills, J. D. et al. LINC00507 is specifically expressed in the primate cortex and has age-dependent expression patterns. J. Mol. Neurosci. 59, 431–439 (2016).

    CAS  PubMed  Google Scholar 

  83. 83.

    Tiedge, H., Chen, W. & Brosius, J. Primary structure, neural-specific expression, and dendritic location of human BC200 RNA. J. Neurosci. 13, 2382–2390 (1993).

    CAS  PubMed  PubMed Central  Google Scholar 

  84. 84.

    Kondrashov, A. V. et al. Inhibitory effect of naked neural BC1 RNA or BC200 RNA on eukaryotic in vitro translation systems is reversed by poly(A)-binding protein (PABP). J. Mol. Biol. 353, 88–103 (2005).

    CAS  PubMed  Google Scholar 

  85. 85.

    Hof, P. R. & Morrison, J. H. The aging brain: morphomolecular senescence of cortical circuits. Trends Neurosci. 27, 607–613 (2004).

    CAS  PubMed  Google Scholar 

  86. 86.

    Morante-Redolat, J. M. & Porlan, E. Neural stem cell regulation by adhesion molecules within the subependymal niche. Front. Cell Dev. Biol. 7, 102 (2019).

    PubMed  PubMed Central  Google Scholar 

  87. 87.

    Barry, G. et al. The long non-coding RNA Gomafu is acutely regulated in response to neuronal activation and involved in schizophrenia-associated alternative splicing. Mol. Psychiatry 19, 486–494 (2014).

    CAS  PubMed  Google Scholar 

  88. 88.

    Bernard, D. et al. A long nuclear-retained non-coding RNA regulates synaptogenesis by modulating gene expression. EMBO J. 29, 3082–3093 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  89. 89.

    Chen, S. et al. LncRNA TUG1 sponges microRNA-9 to promote neurons apoptosis by up-regulated Bcl2l11 under ischemia. Biochem. Biophys. Res. Commun. 485, 167–173 (2017).

    PubMed  Google Scholar 

  90. 90.

    Cocquerelle, C., Mascrez, B., Hetuin, D. & Bailleul, B. Mis-splicing yields circular RNA molecules. FASEB J. 7, 155–160 (1993).

    CAS  PubMed  Google Scholar 

  91. 91.

    Hansen, T. B. et al. Natural RNA circles function as efficient microRNA sponges. Nature 495, 384–388 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  92. 92.

    Memczak, S. et al. Circular RNAs are a large class of animal RNAs with regulatory potency. Nature 495, 333–338 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  93. 93.

    Li, X., Yang, L. & Chen, L. L. The biogenesis, functions, and challenges of circular RNAs. Mol. Cell 71, 428–442 (2018).

    CAS  Google Scholar 

  94. 94.

    Rybak-Wolf, A. et al. Circular RNAs in the mammalian brain are highly abundant, conserved, and dynamically expressed. Mol. Cell 58, 870–885 (2015).

    CAS  PubMed  Google Scholar 

  95. 95.

    Gruner, H., Cortes-Lopez, M., Cooper, D. A., Bauer, M. & Miura, P. CircRNA accumulation in the aging mouse brain. Sci. Rep. 6, 38907 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  96. 96.

    Xu, K. et al. Annotation and functional clustering of circRNA expression in rhesus macaque brain during aging. Cell Discov. 4, 48 (2018).

    PubMed  PubMed Central  Google Scholar 

  97. 97.

    Zhang, Y., Yu, F., Bao, S. & Sun, J. Systematic characterization of circular RNA-associated CeRNA network identified novel circRNA Biomarkers in Alzheimer’s disease. Front. Bioeng. Biotechnol. 7, 222 (2019).

    PubMed  PubMed Central  Google Scholar 

  98. 98.

    Bushman, D. M. et al. Genomic mosaicism with increased amyloid precursor protein (APP) gene copy number in single neurons from sporadic Alzheimer’s disease brains. Elife 4, e05116 (2015).

  99. 99.

    Poulin, J. F., Tasic, B., Hjerling-Leffler, J., Trimarchi, J. M. & Awatramani, R. Disentangling neural cell diversity using single-cell transcriptomics. Nat. Neurosci. 19, 1131–1141 (2016).

    PubMed  Google Scholar 

  100. 100.

    Ximerakis, M. et al. Single-cell transcriptomic profiling of the aging mouse brain. Nat. Neurosci. 22, 1696–1708 (2019).

    CAS  PubMed  Google Scholar 

  101. 101.

    Stahl, P. L. et al. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science 353, 78–82 (2016).

    CAS  PubMed  Google Scholar 

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Acknowledgements

We thank all Lee laboratory members for help and discussion. This study is supported by the Korean Government (MSICT) through National Research Foundation of Korea (NRF) grants NRF-2019R1A3B2067745 to S‐J.V.L. and NRF-2019R1A6A3A01093131 to S.H.

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Ham, S., Lee, SJ.V. Advances in transcriptome analysis of human brain aging. Exp Mol Med 52, 1787–1797 (2020). https://doi.org/10.1038/s12276-020-00522-6

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