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유전자 발현 프로파일링을 통해 노화로 인해 변경되는 수많은 과정이 확인되었지만 이러한 변화가 어떻게 발생하는지는 거의 알려져 있지 않습니다. 여기에서 우리는 야생형 노인 쥐에서 유전자 발현 변화를 유발하는 기본 메커니즘을 밝히기 위해 초기 RNA 시퀀싱과 RNA 중합 효소 II 염색질 면역 침전을 결합한 다음 시퀀싱을 결합했습니다. 우리는 2세 간에서 신장하는 RNA 폴리머라제의 40%가 정지되어 생산적인 전사를 낮추고 유전자 길이에 따라 전사 출력을 왜곡한다는 것을 발견했습니다. 우리는 이러한 전사 스트레스가 내인성 DNA 손상에 의해 발생한다는 것을 입증하고, 특히 영양 감지, 자가포식, 단백질 분해능, 에너지 대사, 면역 기능 및 세포 스트레스와 같은 노화 특징 경로에 영향을 미치는 대부분의 유사분열 후 기관에서 노화에 따른 유전자 발현 변화의 대부분을 설명합니다. 회복력. 연령 관련 전사 스트레스는 선충에서 인간까지 진화적으로 보존됩니다. 따라서, 노화 동안 확률론적 내인성 DNA 손상의 축적은 연령 관련 전사체를 확립하고 주요 노화 특징 경로의 기능 장애를 일으키는 기초 전사를 악화시키며, 이는 DNA 손상이 기능적으로 정상적인 노화의 주요 측면의 기초가 되는 방식을 공개합니다.
노화는 점진적인 분자, 세포 및 생리학적 쇠퇴를 특징으로 하며 그 결과 활력 감소, 노화 관련 질병 및 사망률 증가를 초래합니다. 많은 과정이 나이가 들면서 쇠퇴하거나 변경되기 때문에1 노화에 따른 기본 전사 과정의 기능적 상태에 대해서는 알려진 바가 거의 없습니다. 노화된 쥐와 초파리 뇌는 더 적은 수의 메신저 RNA를 생성하며 전사의 세포 간 변이는 여러 조직에서 증가하는 반면 유전자 간 전사 조정은 노화에 따라 감소합니다7. 그러나 노화에 따른 전사는 주로 유전자 발현 변화와 관련하여 연구됩니다. 전사체학은 노화에 영향을 미치는 수많은 세포 경로와 과정을 식별하는 데 크게 기여했습니다8,9,10. 연령 관련 기관별 유전자 발현 변화의 일부는 전사 인자인 마이크로RNA11,12, 세포 유형 구성 변경8,13 및 후성유전학적 변화14,15로 설명될 수 있지만, 최근의 전사체학 메타 분석에 따르면 노인과 노인 사이의 대부분의 유전자 발현 유사성이 나타났습니다. 마우스 장기는 이러한 알려진 조절 메커니즘에 기인할 수 없습니다8.
DNA 손상 축적은 정상적인 노화16,17 및 앞서 언급한 전사 표현형6,7,18,19의 근본 원인으로 가정되어 왔으며, 이는 주로 DNA 손상 물질 또는 코케인 증후군과 같은 조기 노화 DNA 복구 장애에 노출된 세포와의 유사성을 기반으로 합니다. 삼발성 이영양증. 이러한 상태는 전사 결합 복구(TCR)에 결함이 있어 DNA 병변에서 RNA 중합효소가 정지되도록 하며20 전사 차단 DNA 손상이 정상적인 노화에도 관여할 수 있음을 시사합니다. 내인성 전사 차단 DNA 병변은 정상적인 노화 과정에서 축적되지만21,22,23,24,25 중요한 전사 반응을 유도하는지 여부는 현재 명확하지 않습니다. 이 연구에서 우리는 RNA 중합효소 II(RNAPII) 염색질 면역침전과 이어서 시퀀싱(ChIP-seq) 및 공초점 이미징. 우리는 일반적인 노화 표현형으로 DNA 손상을 축적하여 일반적으로 연령 관련 전사 변화를 일으키고 특히 수명을 결정하는 노화 특징 경로에 영향을 미침으로써 강력한 연령 관련 전사 감소와 전사 출력의 왜곡을 밝힙니다.
정상적인 노화에서의 전사 과정을 조사하기 위해 성인(15주) 및 노령(2세)의 WT 수컷 마우스(그룹당 n = 3)에게 유리딘 유사체인 에티닐-우리딘(EU)을 단일 복강 주사로 투여했습니다. 생체 내에서 새로 합성된 RNA로26. 주사 후 5시간 동안 EU의 형광 염색을 통해 오래된 간에서 EU 신호가 1.5배 감소한 것으로 나타났습니다(그림 1a). 감소는 간 전체에 걸쳐 거의 모든 간세포에 영향을 미쳤으며 연령 관련 배수체화에만 국한되지 않았습니다 (그림 1b). EU 신호의 감소는 RNAPII 의존성 전사 감소를 가리키는 nucleoli (그림 1a)를 제외하고 범핵 이었기 때문에 더 낮은 RNAPII 수준이 전사 감소를 설명 할 수 있는지 여부를 테스트했습니다. 놀랍게도, 동일한 간 샘플을 사용한 RNAPII의 면역형광 염색은 노화된 간에서 감소보다는 1.4배 증가를 나타냈습니다(그림 1c 및 확장 데이터 그림 1a). C 말단 도메인 (CTD)에서 세린 5 잔기 (ser5p)의 인산화로 표시된 RNAPII 개시 및 프로모터 근위 일시 정지는 크게 다르지 않았으며 (그림 1d 및 확장 데이터 그림 1b), 이는 게놈 전체 RNAPII 프로모터 활동 노화가 진행되어도 거의 변하지 않습니다. 그러나 세린 2 CTD 인산화(ser2p)로 표시된 연장된 RNAPII는 1.5배 증가한 것으로 나타났습니다(그림 1e 및 확장 데이터 그림 1c). 이러한 데이터는 노화된 간에서 기본 전사가 변경되었음을 나타냅니다.
90% of RNAPII-dependent nascent RNA production were split into 3 equal bins from its TSS to the transcription termination site (TTS) and corresponding reads from nascent RNA and of RNAPII ChIP–seq mapped in each bin were compared between old and adult liver. Using clustering analysis, we identified genes that were transcriptionally upregulated or downregulated in aging over all bins both in nascent RNA and RNAPII ChIP–seq (Fig. 2f and Extended Data Fig. 3a–c), which reflect promoter regulation. To analyze whether the identified transcriptionally upregulated (n = 778) or downregulated (n = 394) genes are biologically relevant for aging, we used the Enrichr tool for gene set enrichment analysis (GSEA)27,28 to compare these gene signatures with the published aging perturbation database containing 34 mouse liver and 15 rat liver mRNA expression profiles. The transcriptionally upregulated and downregulated gene signatures closely resembled the published rodent liver aging profiles (Fig. 2g,h), indicating that promoter regulatory programs during aging are conserved across transcriptomics studies. In summary, the approximately 1.5-fold lower nascent RNA synthesis in old liver is not due to reduced promoter activity or RNAPII transition to elongation./p>20 kb (x axis) and percentage change between old and adult in EU-seq densities from TSS to 20 kb downstream (y axis) (n = 3,308). i, Percentage stalled RNAPII in gene bodies. The colors indicate the gene-length classes as in Fig. 2e. Data are the mean ± s.d. (10–22 kb: n = 662; 22–30 kb: n = 644; 30–50 kb: n = 788; 50–70 kb: n = 587; 70–110 kb: n = 643; and >110 kb: n = 646)./p>70 kb already comprised approximately 60% of the RNAPII-dependent nascent RNA pool (Extended Data Fig. 3e), long genes disproportionally contributed to reduced nascent RNA levels. The decrease in de novo RNA synthesis and increased RNAPII abundance in gene bodies entail longer residence times and lower transcriptional output of RNAPII. By quantifying the discordance between nascent RNA levels and total RNAPII occupancy (Extended Data Fig. 3f), we estimated an overall approximate 40% nonproductive RNAPII in gene bodies in 2-year-old liver in a gene-length-dependent fashion (Fig. 3i), which implies that they are stalled. Assuming that mouse hepatocytes have a similar number of RNAPII molecules per cell as cultured human fibroblasts33, we believe that the average 2-year-old mouse hepatocyte contains at any time >18,000 stalled RNAPII complexes during elongation (Extended Data Fig. 3g). In summary, liver aging is characterized by a gene-length-dependent, genome-wide loss of transcription elongation and increased RNAPII stalling./p>110 kb from the TSS to 10 kb upstream in Ercc1Δ/− MDFs 24 h after UVC irradiation compared to nonirradiated cells. Black line: >110 kb gene class from normal liver aging data. h, Bias (fraction) of sequencing reads mapping to the coding strand during WT aging from total RNAPII and RNAPII-ser2p ChIP–seq data across all genes (n = 3,809), short (10–22 kb, n = 512) and longest genes (>110 kb, n = 779). P < 0.0001, two-sided unpaired t-test compared to genes with gene length 1–10 kb, 3 mice per group. Data are the mean ± s.e.m. i, Bias (fraction) of sequencing reads mapping to the coding strand during WT aging from total RNAPII and RNAPII-ser2p ChIP–seq data through gene body (3 bins) in all genes and the longest genes (>110 kb, n = 779). Data are the mean ± s.e.m. j, Sequencing read density profiles of the Ghr gene from EU-seq, total RNAPII (all reads aggregated) and total RNAPII split in coding and template strand in WT adult (blue) and aged (red) liver. k, Phosphorylated ATM (red) and γH2A.X (green) in adult and aged mouse liver. Right, Fluorescence intensities shown as box and whisker plots. The center lines show the medians, the box limits mark the IQR, and the whiskers indicate the minimum and maximum values. P = 7.19752 × 10−27 (two-sided unpaired t-test). Counted nuclei: adult n = 313; old n = 315; n = 3 mice per group. Scale bar, 50 μm./p>265-kb long gene frequently downregulated in aged livers across numerous independent studies40, in Xpg−/− and Ercc1Δ/− mutant mice18,34 and in cell cultures exposed to UV light41. Ghr demonstrates a clear GLPT and increased RNAPII abundance across the gene body. We also noticed a 20% shift in reads toward the coding strand in aged livers (Fig. 4j), indicating that Ghr downregulation is the direct result of transcription-blocking lesions. DNA damage-induced RNAPII stalling causes noncanonical DNA damage checkpoint ATM phosphorylation in the absence of double-stranded DNA breaks42, which we also observed in aged livers (Fig. 4k), thereby further demonstrating frequent transcriptional stress in aging. Because the extent of coding strand bias corresponds with the expected level when extrapolated from UV-treated cells38, our data reveal that endogenous transcription-blocking lesions cause RNAPII stalling in a gene-length-dependent manner, which we designated age-related transcriptional stress./p>110 kb, P = 0.000482946). Data are the mean ± s.e.m. P values are from a two-sided unpaired t-test (old versus adult). d, Full transcript abundances (relative to adult) estimated by reads covering 3′UTR from EU-seq of all expressed genes (P = 0.048761825), short (10–22 kb) and long genes (>110 kb, P = 1.78654 × 10−6). Data are the mean ± s.e.m., P values are from a two-sided unpaired t-test. e, Significant overrepresented pathways in TShigh genes by IPA, KEGG, Reactome and GSEA-hallmarks classified by main process category (bold). Aggregated P values were obtained from a Fisher's exact test. See Supplementary Table 2 for detailed pathway information./p>1.5-fold first-to-last exon transcriptional loss in aging (n = 830), representing genes with high transcriptional stress levels (TShigh), for functional examination. Notably, we found a highly significant overlap with the overall profiles of six independent studies representing downregulated mRNAs after UVC-induced DNA damage (Supplementary Table 1), further supporting the link between transcription-blocking DNA lesions and age-related transcriptional stress. Functional examination identified several significantly overrepresented cellular pathways previously classified as hallmarks of aging1 (Fig. 5e and Supplementary Table 2), such as the nutrient sensing pathways IGF1, insulin, growth hormone and mTOR signaling, which are all known to influence life span1,44. Autophagy, the unfolded protein response and the endoplasmic reticulum stress pathway were also identified, linking transcriptional stress to loss of proteostasis. Furthermore, we found key energy metabolic processes such as oxidative phosphorylation and pyruvate metabolism, which were functionally reduced by transcriptional stress in the livers of Ercc1Δ/− mice26. Additional identified processes included immune factors, fatty acid metabolism and the NRF2 antioxidant pathway, which are all causally involved in life span and/or age-related diseases47,48,49,50. In conclusion, transcriptional stress appears to be a critical cause of deregulation of aging hallmark pathways and processes in WT aging mice./p>1 dataset of a tissue was present, the mean ± s.d. and aggregated P value (Fisher's exact test) are shown./p>8) was used for further analyses. Total RNA sequencing was performed as described elsewhere62. To selectively isolate EU-labeled nascent RNA, we used the Click-iT nascent RNA Capture Kit (cat. no. c10365, Thermo Fisher Scientific): biotin azide was attached to the ethylene groups of the EU-labeled RNA using Click-iT chemistry. The EU-labeled nascent RNA was purified using MyOne Streptavidin T1 magnetic beads. Captured EU-RNA attached on streptavidin beads was immediately subjected to on-bead sequencing library generation using the TruSeq mRNA Sample Preparation Kit v2 (Illumina) according to the manufacturer's protocols with modifications. The first steps of the protocol were skipped; directly on-bead complementary DNA (cDNA) was synthesized by reverse transcriptase (Super-Script II) using random hexamer primers. The cDNA fragments were then blunt-ended through an end-repair reaction, followed by dA-tailing. Subsequently, specific double-stranded barcoded adapters were ligated and library amplification for 15 cycles was performed. PCR libraries were cleaned up, measured on an Agilent Bioanalyzer using the DNA1000 assay, pooled at equal concentrations and sequenced per three in one lane on a HiSeq 2500./p>90% of all EU-seq reads are mapped to these genes). Ercc1Δ/− mice (k = 20, n = 2,430); Xpg−/− mice (k = 20, n = 3,842); UV-treated Ercc1Δ/− MDFs (k = 10, n = 1974); WT aging kidney (k = 20, n = 2,135); human tendon (k = 5, n = 773); C. elegans (k = 5, n = 2,872). To match WT aging EU-seq data with the corresponding RNAPII ChIP–seq data (generated from the same liver), the corresponding genes from the ‘all expressed genes’ gene set were also selected in the RNAPII ChIP–seq datasets. The intra-sample-specific background was determined by calculating the reads in the intergenic regions and proportionally removed. The overall background signal was subtracted using the DNA input samples. To biologically define the ‘all expressed genes’ (n = 3,970) in WT aging, we performed a k-mean clustering analysis combined with EU-seq and total ChIP–seq reads between adult and old samples. Under the criterion describes in Extended Data Fig. 3a, we defined the four main patterns found in k-mean cluster analysis as four biological groups: promoter-upregulated genes, n = 778 (EU-seq and RNAPII ChIP–seq level increased across three bins); promoter-downregulated genes, n = 394 (EU-seq and RNAPII ChIP–seq level decreased across three bins); GLPThigh genes, n = 914 (steep EU-seq level progressive decrease, steep RNAPII ChIP–seq level increase across three bins); remainder genes, n = 1,884 (mild EU-seq level progressive decrease, mild RNAPII ChIP–seq increase across three bins). To study the relationship between gene length and transcriptional stress phenotype, the expressed genes (n = 3,970) in WT mice were divided into six groups according to their length, each containing a similar number of genes: 10–22 kb (n = 662, average = 16.47 kb, median = 16.75 kb); 22–30 kb (n = 644, average = 26.87 kb, median = 26.94 kb); 30–50 kb (n = 788, average = 40.19 kb, median = 39.68 kb); 50–70 kb (n = 587, average = 59.18 kb, median = 59.02 kb); 70–110 kb (n = 643, average = 87.93 kb, median = 86.75 kb) and >110 kb (n = 646, average = 199.47 kb, median = 160 kb). In figures measuring gene class behavior, we first calculated the per gene the average signal from n = 3 mice followed by averaging the signal for all genes in the gene class./p>8 weeks, old is >14 months and age difference between young/adult and aged organs is >6 months (mouse, rat); human old is >56 years with at least an age difference of >12 years. We adopted a threshold FDR < 0.05. Aggregated P values for the main identified biological processes were calculated by combining the P values of the corresponding detected subpathways using Fisher's exact test./p>400-fold surplus of biotin for every incorporated EU in nascent RNA in the Click-iT reaction, the reaction is saturated or follows the same asymptote; (2) only one EU incorporation per RNA molecule is sufficient to isolate that specific molecule; (3) EU incorporation is a stochastic process in which the concentration of available EU in the total nucleotide pool linearly correlates with the distance between EU molecules in the nascent RNAs. If there is an EU availability difference between adult and old mice, it is expected that in short RNA species (≤300 nucleotides) the probability of at least one EU incorporation is significantly lower and thus we would empirically observe a lower percentage sequence read mapping to such small RNA species in aged liver. The process of EU incorporation was modeled into nascent RNA species by means of a Poisson process. Specifically, one can think of the number of EU incorporations into nascent RNA as a Poisson process not in time, as it is generally used, but in length as measured in nucleotides. Mathematically, if X(t) is a Poisson process then the probability that there is no event in a time interval (0,t) reads exp(-λt) where λ is the intensity of the Poisson process. Equally, the probability that there is at least one event in the time interval (0,t) is thus 1 − exp(-λt). For each RNA species in our specified RNA length classes identified in the EU-seq datasets, the probability that at least one EU has been incorporated was subsequently computed using the formula above. Clearly, since \(1 - {\mathrm{e}}^{ - {\textstyle{1 \over x}}} > 1 - {\mathrm{e}}^{ - {\textstyle{1 \over y}}}\) for all x < y, Poisson processes with higher intensity will necessarily exhibit a larger probability that at least one EU has been incorporated than Poisson processes of lower intensity. Three groups of RNA species were examined: (1) ≤300 nucleotides (number of RNA species n = 7,932); (2) between 1,000 and 3,000 nucleotides (number of RNA species, n = 1983); and (3) between 2,000 and 4,000 nucleotides (number of species, n = 1,802). The number of RNA species reflect the total number present in the Mus musculus genome database (Ensembl). The latter two classes, although still representing short RNA species, are incorporated as a positive control in which a difference, if there is 1.5-fold less EU available, is not expected. In all cases, the probability vectors were not Gaussian as calculated by Kolmogorov–Smirnov test; thus, for each fixed intensity of the underlying Poisson process, the median and interquartile range (IQR) for the probability that at least one EU is incorporated are calculated. Significance between 1.5-fold-apart intensities was calculated by the Mann–Whitney U-test./p>110 kb (red; n = 646). Data are mean ± SEM. P-value = 7.84163*10−22, two-sided unpaired t-test. e, The contribution (%) of each gene-length class to the total nascent RNA pool in adult samples. f, Calculation of the fraction of unproductive RNAPII complexes in aged liver. g, Estimation of the number of stalling RNAPII complexes in aged liver./p>110 kb). Data are mean ± SEM. Note that the strand bias is only present in MCF7 cells 1 hour after UVB treatment, when RNAPII is still stalled on DNA lesions and DNA repair is ongoing. After 6 hours, most of the stalled RNAPII has been removed from the DNA lesions. This shows that i) the protocol used is able to detect a bias towards the coding strand and therefore can be used to analyze aging samples, ii) the coding strand bias is a transient phenotype after UVB. Based on published amounts of coding strand bias after a known UVC-induced DNA lesion density38, we estimate that livers from wildtype aged mice display a coding strand bias fraction in the range of 0.05–0.10. b–f, Mean local DNA methylation coverage (b) and (c–f) local nucleotide composition status in template strands of 50 genes with that exhibit the highest coding strand bias in general. The intragenic intronic region is chosen with the highest coding strand bias (high strand bias loci). This loci gene set is compared t i) random selected intragenic loci of similar size: 6 times 50 random intronic locations in the template strand, and ii) the complete intronic transcriptome; all introns from transcriptome (including high strand bias locations). Average of n = 50 / group shown. Data are mean ± SD./p>
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