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• W4221058870 abstract "Resource7 March 2022Open Access Transparent process Ageing induces tissue-specific transcriptomic changes in Caenorhabditis elegans Xueqing Wang Xueqing Wang orcid.org/0000-0003-3136-2568 State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai, China University of Chinese Academy of Sciences, Beijing, China Contribution: Resources, Data curation, Formal analysis, Validation, ​Investigation, Visualization, Methodology, Writing - original draft, Writing - review & editing Search for more papers by this author Quanlong Jiang Quanlong Jiang CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), Peking University, Beijing, China Contribution: Data curation, Software, Formal analysis, Validation, ​Investigation, Visualization, Writing - original draft, Writing - review & editing Search for more papers by this author Yuanyuan Song Yuanyuan Song State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai, China University of Chinese Academy of Sciences, Beijing, China Contribution: Data curation, Formal analysis, ​Investigation Search for more papers by this author Zhidong He Zhidong He State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai, China University of Chinese Academy of Sciences, Beijing, China Search for more papers by this author Hongdao Zhang Hongdao Zhang orcid.org/0000-0003-2390-0450 State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai, China University of Chinese Academy of Sciences, Beijing, China Contribution: Data curation, Formal analysis, ​Investigation Search for more papers by this author Mengjiao Song Mengjiao Song State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai, China University of Chinese Academy of Sciences, Beijing, China Contribution: ​Investigation, Visualization Search for more papers by this author Xiaona Zhang Xiaona Zhang orcid.org/0000-0001-6479-3562 State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai, China University of Chinese Academy of Sciences, Beijing, China Contribution: ​Investigation Search for more papers by this author Yumin Dai Yumin Dai State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai, China University of Chinese Academy of Sciences, Beijing, China Contribution: ​Investigation Search for more papers by this author Oezlem Karalay Oezlem Karalay Max Planck Institute for Biology of Ageing, Cologne, Germany Cologne Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne, Cologne, Germany Contribution: ​Investigation Search for more papers by this author Christoph Dieterich Christoph Dieterich Klaus Tschira Institute for Integrative Computational Cardiology and Department of Internal Medicine III, University Hospital Heidelberg, Heidelberg, Germany Contribution: Data curation, Software, Formal analysis, ​Investigation Search for more papers by this author Adam Antebi Adam Antebi Max Planck Institute for Biology of Ageing, Cologne, Germany Cologne Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne, Cologne, Germany Contribution: Data curation, Formal analysis, Supervision, Writing - original draft Search for more papers by this author Ligang Wu Corresponding Author Ligang Wu [email protected] orcid.org/0000-0003-4010-9118 State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai, China University of Chinese Academy of Sciences, Beijing, China Contribution: Supervision, Funding acquisition, Validation, Writing - original draft, Project administration Search for more papers by this author Jing-Dong J Han Corresponding Author Jing-Dong J Han [email protected] orcid.org/0000-0002-9270-7139 CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), Peking University, Beijing, China Contribution: Supervision, Funding acquisition, Validation, Writing - original draft, Project administration Search for more papers by this author Yidong Shen Corresponding Author Yidong Shen [email protected] orcid.org/0000-0002-2841-7233 State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai, China University of Chinese Academy of Sciences, Beijing, China Contribution: Conceptualization, Data curation, Formal analysis, Supervision, Funding acquisition, Validation, ​Investigation, Methodology, Writing - original draft, Project administration, Writing - review & editing Search for more papers by this author Xueqing Wang Xueqing Wang orcid.org/0000-0003-3136-2568 State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai, China University of Chinese Academy of Sciences, Beijing, China Contribution: Resources, Data curation, Formal analysis, Validation, ​Investigation, Visualization, Methodology, Writing - original draft, Writing - review & editing Search for more papers by this author Quanlong Jiang Quanlong Jiang CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), Peking University, Beijing, China Contribution: Data curation, Software, Formal analysis, Validation, ​Investigation, Visualization, Writing - original draft, Writing - review & editing Search for more papers by this author Yuanyuan Song Yuanyuan Song State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai, China University of Chinese Academy of Sciences, Beijing, China Contribution: Data curation, Formal analysis, ​Investigation Search for more papers by this author Zhidong He Zhidong He State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai, China University of Chinese Academy of Sciences, Beijing, China Search for more papers by this author Hongdao Zhang Hongdao Zhang orcid.org/0000-0003-2390-0450 State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai, China University of Chinese Academy of Sciences, Beijing, China Contribution: Data curation, Formal analysis, ​Investigation Search for more papers by this author Mengjiao Song Mengjiao Song State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai, China University of Chinese Academy of Sciences, Beijing, China Contribution: ​Investigation, Visualization Search for more papers by this author Xiaona Zhang Xiaona Zhang orcid.org/0000-0001-6479-3562 State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai, China University of Chinese Academy of Sciences, Beijing, China Contribution: ​Investigation Search for more papers by this author Yumin Dai Yumin Dai State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai, China University of Chinese Academy of Sciences, Beijing, China Contribution: ​Investigation Search for more papers by this author Oezlem Karalay Oezlem Karalay Max Planck Institute for Biology of Ageing, Cologne, Germany Cologne Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne, Cologne, Germany Contribution: ​Investigation Search for more papers by this author Christoph Dieterich Christoph Dieterich Klaus Tschira Institute for Integrative Computational Cardiology and Department of Internal Medicine III, University Hospital Heidelberg, Heidelberg, Germany Contribution: Data curation, Software, Formal analysis, ​Investigation Search for more papers by this author Adam Antebi Adam Antebi Max Planck Institute for Biology of Ageing, Cologne, Germany Cologne Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne, Cologne, Germany Contribution: Data curation, Formal analysis, Supervision, Writing - original draft Search for more papers by this author Ligang Wu Corresponding Author Ligang Wu [email protected] orcid.org/0000-0003-4010-9118 State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai, China University of Chinese Academy of Sciences, Beijing, China Contribution: Supervision, Funding acquisition, Validation, Writing - original draft, Project administration Search for more papers by this author Jing-Dong J Han Corresponding Author Jing-Dong J Han [email protected] orcid.org/0000-0002-9270-7139 CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), Peking University, Beijing, China Contribution: Supervision, Funding acquisition, Validation, Writing - original draft, Project administration Search for more papers by this author Yidong Shen Corresponding Author Yidong Shen [email protected] orcid.org/0000-0002-2841-7233 State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai, China University of Chinese Academy of Sciences, Beijing, China Contribution: Conceptualization, Data curation, Formal analysis, Supervision, Funding acquisition, Validation, ​Investigation, Methodology, Writing - original draft, Project administration, Writing - review & editing Search for more papers by this author Author Information Xueqing Wang1,2,†, Quanlong Jiang3,4,†, Yuanyuan Song1,2,†, Zhidong He1,2, Hongdao Zhang1,2, Mengjiao Song1,2, Xiaona Zhang1,2, Yumin Dai1,2, Oezlem Karalay5,6, Christoph Dieterich7, Adam Antebi5,6, Ligang Wu *,1,2, Jing-Dong J Han *,3,4 and Yidong Shen *,1,2 1State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai, China 2University of Chinese Academy of Sciences, Beijing, China 3CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China 4Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), Peking University, Beijing, China 5Max Planck Institute for Biology of Ageing, Cologne, Germany 6Cologne Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne, Cologne, Germany 7Klaus Tschira Institute for Integrative Computational Cardiology and Department of Internal Medicine III, University Hospital Heidelberg, Heidelberg, Germany † These authors contributed equally to this work *Corresponding author. Tel: +86 21 54921321; E-mail: [email protected] *Corresponding author. Tel: +86 10 62757330; E-mail: [email protected] *Corresponding author. Tel: +86 21 54921171; E-mail: [email protected] The EMBO Journal (2022)41:e109633https://doi.org/10.15252/embj.2021109633 PDFDownload PDF of article text and main figures. Peer ReviewDownload a summary of the editorial decision process including editorial decision letters, reviewer comments and author responses to feedback. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions Figures & Info Abstract Ageing is a complex process with common and distinct features across tissues. Unveiling the underlying processes driving ageing in individual tissues is indispensable to decipher the mechanisms of organismal longevity. Caenorhabditis elegans is a well-established model organism that has spearheaded ageing research with the discovery of numerous genetic pathways controlling its lifespan. However, it remains challenging to dissect the ageing of worm tissues due to the limited description of tissue pathology and access to tissue-specific molecular changes during ageing. In this study, we isolated cells from five major tissues in young and old worms and profiled the age-induced transcriptomic changes within these tissues. We observed a striking diversity of ageing across tissues and identified different sets of longevity regulators therein. In addition, we found novel tissue-specific factors, including irx-1 and myrf-2, which control the integrity of the intestinal barrier and sarcomere structure during ageing respectively. This study demonstrates the complexity of ageing across worm tissues and highlights the power of tissue-specific transcriptomic profiling during ageing, which can serve as a resource to the field. Synopsis Whether organismal ageing is associated with specific changes in different tissues and organs remains poorly addressed. Here, dissection and transcriptomic analyses of diverse C. elegans tissues provides insight into the disting ageing trajectories and regulators in each of them. Five major somatic tissues—nervous system, hypodermis, intestine, coelomocyte and body all muscle - undergo distinct transcriptomic changes during C. elegans ageing. Tissue-specific changes between young and old worms are more pronounced than common changes in different tissues. Ageing regulates similar biological processes with different gene sets and regulators across tissues. Transcription factors irx-1/IRX and myrf-2/MYRF control ageing of intestine and body wall muscle, respectively. Introduction Ageing causes a systematic decline in physiological functions across tissues of the body and therefore poses the main risk for various severe diseases in the aged population (Campisi et al, 2019). Over the past decades, the mechanisms underlying ageing and the cellular systems that protect the organism have been substantially charted by pioneering studies in model organisms (Kenyon, 2010; Riera et al, 2016; Campisi et al, 2019). The nematode Caenorhabditis elegans is among the foremost models in ageing research, with a short lifespan of only a few weeks, but displaying similar ageing phenotypes and causes as humans (Mack et al, 2018). It was first shown in C. elegans that longevity is regulated by evolutionarily conserved signalling with the identification of the insulin/IGF-1 signalling (IIS) pathway (Johnson, 1990; Kenyon et al, 1993; Morris et al, 1996; Ogg et al, 1997; Kenyon, 2011). Mutations in critical IIS genes, such as FOXO3A, were later found to be associated with human longevity (Willcox et al, 2008). Since then, studies with C. elegans have also pioneered numerous other milestone findings in the biology of ageing (Kenyon, 2010; Mack et al, 2018; Campisi et al, 2019). Ageing or longevity interventions induce evident transcriptomic changes in C. elegans and other organisms (Lund et al, 2002; McElwee et al, 2003; Murphy et al, 2003; Hou et al, 2016; Li et al, 2019). These changes are proposed to represent the molecular causes underlying longevity (Murphy et al, 2003). The transcriptional information in previous C. elegans ageing studies was mainly from whole animals of different ages or longevity mutants due to the technical difficulty of dissecting the tissues of interest from the tiny worm (Corsi et al, 2015). These datasets have provided key information and insights into ageing to researchers using C. elegans as well as other model organisms. Nevertheless, ageing cannot be solely analysed at the level of whole animal, since each tissue has its characteristic ageing trajectory, driven by common and unique ageing processes (Rando & Wyss-Coray, 2021). In addition, tissue–tissue communication also plays an essential role at the systemic level, but how this occurs and the relationship among such tissues remain elusive. In order to decipher autonomous and non-autonomous ageing mechanisms in multicellular organisms, it is critical to profile tissue-specific transcriptomic changes during ageing. Yet, the lack of age-related transcriptomic information in the worm tissues poses a significant gap in our understanding. By improving the protocol to isolate embryonic worm neurons by fluorescence-activated cell sorting (FACS) (Von Stetina et al, 2007), the Murphy lab isolated GFP-labelled cells, enabling transcriptomic profiling in adult tissues (Kaletsky et al, 2016, 2018). Following their technique, we further developed a method to isolate cells from body wall muscle by micromanipulation (Zhou et al, 2019). In the current study, we isolated cells from five key somatic tissues (i.e. neuron, intestine, body wall muscle, hypodermis and coelomocytes) from worms at different ages and thereby profiled tissue-specific transcriptomic changes during worm ageing. From the perspective of differentially expressed genes, the biological pathways and the underlying regulators, our results indicate remarkable diversity and complexity of ageing across tissues. The various transcriptomic changes not only arise from tissue characteristic structures and functions but also from different regulations of the same biological processes across tissues. Following the atlas of transcriptomic changes among ageing tissues, we further identified two transcription factors, irx-1 controlling the integrity of the intestine wall and myrf-2 driving the deterioration of sarcomere. These datasets provide a foundation to further explore the tissue-specific and systemic machinery of ageing. Results Sequencing of major somatic tissues in young and aged C. elegans To profile the transcriptomes in various somatic tissues, worms labelled with tissue-specific fluorescent markers were dissociated. The cells in intestine, body wall muscle (BWM), hypodermis and coelomocyte are of similar identities and were hand-picked by micromanipulation (Zhou et al, 2019). Neurons are composed of distinct classes and were collected by FACS (Kaletsky et al, 2016). The worms at day 1 of adulthood (D1) were considered as young, whereas the post-reproductive worms at day 8 of adulthood (D8) as aged. The isolated cells from both young and aged worms were subjected to Smart-seq2 for the tissue-specific transcriptomic changes with ageing (Picelli et al, 2013) (Fig 1A). Cell viability was validated by fluorescent indicators (Zhang et al, 2011) (Fig EV1A). A battery of tissue-specific genes was examined by qRT–PCR to assess the purity of the analysed samples (Fig EV1B). In each type of manually picked cells from intestine, BWM, hypodermis and coelomocytes, the expression of corresponding tissue-specific genes was detected, but not the genes expressed in other tissues (Fig EV1C–F), indicating that the isolated cells were largely devoid of contamination. With a similar purity to previous reports (Kaletsky et al, 2016, 2018), the FACS-isolated neuron samples were highly enriched of neurons and devoid of other tissues (Fig EV1G). Figure 1. Worm tissues undergo remarkable transcriptomic changes during ageing A. The flowchart depicting the transcriptomic profiling of isolated worm tissues. Isolated cells from each tissue were pooled for Smart-seq2. B, C. Principal component analysis (B) and hierarchical clustering (C) of the transcriptomic datasets of different tissues from young and aged worms in three biological replicates. D1: day 1 of adulthood, D8: day 8 of adulthood. Neu: neuron, Int: intestine, BWM: body wall muscle, Hyp: hypodermis, Coel: coelomocyte. D. The volcano plots showing the differentially expressed genes in ageing tissues. Genes with a fold change > 1.5 and a P-value smaller than 0.05 were considered significantly changed by ageing. The numbers and percentages of Age-DEGs in expressed genes are shown in the corresponding plots. Download figure Download PowerPoint Click here to expand this figure. Figure EV1. The validation of isolated tissues for transcriptomic profiling A. Cell viability assay of isolated worm cells. Cells from neurons, intestine, body wall muscle and hypodermis were labelled with tissue-specific GFP reporters. Dead cells in their preparations (RFP) were labelled by ethidium homodimer-1 staining (RFP). In the preparation of coelomocytes, coelomocytes and live cells were labelled with RFP and Calcein AM (GFP) respectively. Arrow heads denote the cells of interest. Scale bar: 20 µm. B. The list of tissue-specific genes used. C–F. RT–qPCR of indicated tissue-specific genes in the isolated intestine (C), body wall muscle (D), hypodermis (E) and coelomocytes (F). ND: Ct > 40. Mean ± SD. n = 3 biological replicates. G. RT–qPCR (left) or RNA-Seq reads (right) of tissue-specific genes indicate that the isolated neurons are of comparable purity as previously reported. ND: Ct > 40. D1: day 1 of adulthood. D8: day 8 of adulthood. Mean ± SD. n = 3 biological replicates. Unpaired t-test, two-tailed. Download figure Download PowerPoint As expected, each tissue underwent significant and different transcriptomic changes during ageing (Figs 1B–D and EV2A). Principal component analysis and hierarchical clustering showed that the sequenced samples were clustered first by their tissue types and then by their ages (Fig 1B and C), highlighting the distinctive transcriptomes among worm tissues as reported (Kaletsky et al, 2018) and the remarkable effects of ageing on gene expression. When compared to their D1 counterparts, 3,744, 2,246, 1,189, 902 and 1,056 genes were differentially expressed in the D8 neurons, intestine, BWM, hypodermis and coelomocytes respectively (Fig 1D and Dataset EV1). These ageing-regulated differentially expressed genes (Age-DEGs) comprise 21.5, 18.4, 7.4, 11.5 and 7.7% of the expressed genes in these tissues (Fig 1D) respectively. 50.1% of Age-DEGs have orthologs in mammals (Fig EV2B and Dataset EV1), further indicating that worms share similar ageing mechanisms with humans (Kenyon, 2010; Campisi et al, 2019). Click here to expand this figure. Figure EV2. Ageing induces significant transcriptomic changes across C. elegans tissues A heatmap depicting the transcriptomic changes in the indicated tissues during ageing. Age-DEGs in worm tissues are highly conserved. A comparison of Age-DEGs in the indicated tissues. Download figure Download PowerPoint Different tissues exhibit distinct transcriptomic changes during ageing Worm tissues have distinct structures and functions that deteriorate with ageing. To explore whether their ageing-induced transcriptomic changes are also different from each other, we first compared Age-DEGs from each tissue with those from the whole worm at D1 and D7. Only a small fraction of the Age-DEGs in the tested tissues was also altered at the worm level during ageing (Figs 2A and B and EV3C). A comparison with a reported dataset from worms at D2 and D8 showed similar results (Hou et al, 2016) (Fig EV3A, B and D). Therefore, each tissue could have its transcriptional signature during ageing. Figure 2. The ageing-induced transcriptomic changes are highly variable across tissues A, B. The overlap of upregulated (A) and downregulated (B) ageing-regulated differentially expressed genes (Age-DEGs) in the worm and the analysed tissues. The number and ratio of Age-DEGs are shown in the corresponding Venn diagrams. Hypergeometric test. C. The overlap of Age-DEGs among tissues. D. The number of common upregulated (left) and downregulated (right) Age-DEGs drops with the increase in analysed tissues. The X-axis denotes the numbers of analysed tissues. Download figure Download PowerPoint Click here to expand this figure. Figure EV3. Age-DEGs in the whole worm have a limited overlap with those in individual tissues A, B. The overlap of upregulated (A) and downregulated (B) Age-DEGs in the worm and the analysed tissues. The Age-DEGs in the worm are from a previous report by Hou et al (2016), by comparing the transcriptomes of WT worms at day 2 and day 8 of adulthood. The number and ratio of Age-DEGs are shown in the corresponding Venn diagrams. Hypergeometric test. C, D. The comparison of Age-DEGs in the indicated samples. (C): The analysis using the worm RNA-Seq data obtained in this study (Worm). (D): The analysis using the reported worm RNA-Seq data by Hou et al (2016). (Worm (H)). Red lines denote the common Age-DEGs between the whole worm and individual tissues. Download figure Download PowerPoint To pursue this hypothesis, we further compared the Age-DEGs among the five tissues. As speculated, each tissue showed a substantial fraction of unique Age-DEGs (Figs EV2C and EV3C and D). 82.8, 72.7, 61.7, 65.7 and 74.1% of the upregulated Age-DEGs in neuron, intestine, BWM, hypodermis and coelomocyte were specific in corresponding tissues (Figs 2C and EV2C). The ratios of tissue-specific downregulated Age-DEGs were 74.1, 69.3, 67.2, 59.3 and 57.6%, respectively, in neuron, intestine, BWM, hypodermis and coelomocyte (Figs 2C and EV2C). The expression patterns of these tissue-specific Age-DEGs are not necessarily restricted to the corresponding tissues because most genes were found to express across tissues (Appendix Fig S1A–F). Therefore, ageing could regulate the same genes differently among various tissues. Following the high tissue specificities of Age-DEGs, the common Age-DEGs among tissues were limited (Fig 2C and D). The number of common Age-DEGs exhibited a remarkable decrease with the increase in analysed tissues (Fig 2D). We surprisingly found that there were no upregulated Age-DEGs shared by all five tissues, whereas there were only three common downregulated Age-DEGs in these tissues (Fig 2C and D, Dataset EV1). Therefore, ageing is unlikely to alter a common core group of genes across various tissues. Taken together, these results indicate high tissue specificity in the ageing-induced transcriptomic changes. The transcriptomic changes portray the ageing processes among tissues Following Age-DEGs, we next used WormCat, a powerful tool for analysing and comparing multiple C. elegans gene datasets, to examine the functional changes in ageing worm tissues (Holdorf et al, 2020). WormCat categorises a gene first by its physiological function (Category 1) and then by molecular function or cellular location (Categories 2 and 3) (Holdorf et al, 2020). By Category 1, many of the ageing-regulated biological processes (Age-BPs) were shared among multiple examined tissues and the whole worm. 74.2% upregulated and 73.9% downregulated Age-BPs were identified in at least two tissues (Fig 3A and B, and Dataset EV2). Among them are some well-known ageing hallmarks, such as stress response and metabolism (Lopez-Otin et al, 2013). Interestingly, many of these Age-BPs were both up- and downregulated during ageing, implying a complex impact of ageing. Figure 3. The enriched gene sets in the transcriptomes of ageing worms and worm tissues A, B. The enriched gene sets from upregulated (A) and downregulated (B) Age-DEGs analysed by WormCat. Categories 1 and 2 are differentiated by capitalisation and bold fonts. Category 2 are shown below the corresponding Category 1. The numbers of tissues enriched with indicated WormCat Category 1 are shown in the cyan blocks. Download figure Download PowerPoint When examined in more detailed Categories 2 and 3, different facets of these general biological functions were shown to change differently during ageing (Fig 3 and Dataset EV2). As Age-DEGs exhibit high tissue specificities (Fig 2), each tissue consistently showed distinct sets of Age-BPs at Categories 2 and 3. At Category 2, 62.0 and 58.2% of up- and downregulated Age-BPs, respectively, were detected in only one tissue, whereas the corresponding ratios of tissue-specific Age-BPs at Category 3 were, respectively, 71.9 and 64.6% (Dataset EV2). Gene ontology (GO) analys" @default.
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• W4221058870 title "Ageing induces tissue‐specific transcriptomic changes in <i>Caenorhabditis elegans</i>" @default.
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• W4221058870 cites W4221058870 @default.
• W4221058870 doi "https://doi.org/10.15252/embj.2021109633" @default.
• W4221058870 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/35253240" @default.
• W4221058870 hasPublicationYear "2022" @default.
• W4221058870 type Work @default.

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