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Aging induces region-specific dysregulation of hormone synthesis in the primate adrenal gland

Nature Aging volume 4, pages 396–413 (2024)Cite this article

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Abstract

Adrenal glands, vital for steroid secretion and the regulation of metabolism, stress responses and immune activation, experience age-related decline, impacting systemic health. However, the regulatory mechanisms underlying adrenal aging remain largely uninvestigated. Here we established a single-nucleus transcriptomic atlas of both young and aged primate suprarenal glands, identifying lipid metabolism and steroidogenic pathways as core processes impacted by aging. We found dysregulation in centripetal adrenocortical differentiation in aged adrenal tissues and cells in the zona reticularis region, responsible for producing dehydroepiandrosterone sulfate (DHEA-S), were highly susceptible to aging, reflected by senescence, exhaustion and disturbed hormone production. Remarkably, LDLR was downregulated in all cell types of the outer cortex, and its targeted inactivation in human adrenal cells compromised cholesterol uptake and secretion of dehydroepiandrosterone sulfate, as observed in aged primate adrenal glands. Our study provides crucial insights into endocrine physiology, holding therapeutic promise for addressing aging-related adrenal insufficiency and delaying systemic aging.

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Fig. 1: Accumulation of aging-related adrenal damage in cynomolgus monkeys.
Fig. 2: Cellular and molecular alterations in aged NHP adrenals.
Fig. 3: Obstructed centripetal cortical differentiation in aged NHP adrenals.
Fig. 4: Augmented transcriptional noise in aged NHP adrenal glands.
Fig. 5: Deciphering molecular alterations of the aged adrenocortical cells.
Fig. 6: LDLR loss decreases DHEA-S secretion in aged NHP adrenals.

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Data availability

The raw sequence data reported in this paper have been deposited in the Genome Sequence Archive in National Genomics Data Center, China National Center for Bioinformation, with accession number HRA004042 (human adrenocortical cells, https://ngdc.cncb.ac.cn/gsa-human/browse/HRA004042) and CRA009996 (monkey adrenal glands, https://ngdc.cncb.ac.cn/gsa/browse/CRA009996). The reference genomes of Macaca_fascicularis_6.0 and hg19 were downloaded from ensembl genome database (https://ensembl.org/). Source data are provided with this paper. All other data supporting the findings of this study are available from the corresponding authors upon reasonable request.

Code availability

The code used to perform bioinformatics analysis in this study is available at GitHub (https://github.com/wxb1998/R_adrenal).

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Acknowledgements

We thank Y. Liu, X. Xu, D. Huang, C. Liang and Z. Liu for their help in the experimental conduction. We thank J. Jia and S. Meng from the Institute of Biophysics, Chinese Academy of Sciences for their help in fluorescence-activated cell sorting (FACS), and S. Li from the Institute of Zoology, Chinese Academy of Sciences for her help in image scanning of immunohistochemical staining. We are also grateful to L. Bai, J. Chen, Q. Chu, J. Lu, Y. Yang, R. Bai, L. Tian and X. Li for administrative assistance. This work was supported by the National Key Research and Development Program of China (2022YFA1103700), the National Natural Science Foundation of China (82125011, 81921006, 92149301, 82122024, 92168201, 92049116 and 82361148131), the National Key Research and Development Program of China (2020YFA0804000, 2020YFA0112200, 2021YFF1201000, 2022YFA1103800, the STI2030-Major Projects-2021ZD0202400 and 2021YFA1101000), the National Key Research and Development Program of China (82330044, 32341001, 92049304, 32121001, 82192863, 82361148130, 82301758, 82271600, 82322025 and 82071588), CAS Project for Young Scientists in Basic Research (YSBR-076 and YSBR-012), the Program of the Beijing Natural Science Foundation (Z230011), the Informatization Plan of Chinese Academy of Sciences (CAS-WX2022SDC-XK14), New Cornerstone Science Foundation through the XPLORER PRIZE (2021-1045), Youth Innovation Promotion Association of CAS (E1CAZW0401, 2022083 and 2023092), Young Elite Scientists Sponsorship Program by CAST (2021QNRC001), Excellent Young Talents Program of Capital Medical University (12300927), the Project for Technology Development of Beijing-affiliated Medical Research Institutes (11000023T000002036310), and Excellent Young Talents Training Program for the Construction of Beijing Municipal University Teacher Team (BPHR202203105), the Quzhou Technology Projects (2022K46).

Author information

Author notes

  1. These authors contributed equally: Qiaoran Wang, Xuebao Wang, Beibei Liu, Shuai Ma, Feng Zhang.

Authors and Affiliations

  1. CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, China

    Qiaoran Wang, Beibei Liu, Yanling Fan, Yingjie Ding, Muzhao Xiong, Jiaming Li & Weiqi Zhang

  2. University of Chinese Academy of Sciences, Beijing, China

    Qiaoran Wang, Xuebao Wang, Yingjie Ding, Muzhao Xiong, Jiaming Li, Yandong Zheng, Chengyu Liu, Jing Qu, Guang-Hui Liu & Weiqi Zhang

  3. State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, China

    Xuebao Wang, Yandong Zheng, Chengyu Liu & Jing Qu

  4. Key Laboratory of Organ Regeneration and Reconstruction, Institute of Zoology, Chinese Academy of Sciences, Beijing, China

    Xuebao Wang, Shuai Ma, Shuhui Sun, Yaobin Jing, Yandong Zheng, Chengyu Liu, Jing Qu & Guang-Hui Liu

  5. Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, China

    Xuebao Wang, Shuai Ma, Shuhui Sun, Yandong Zheng, Chengyu Liu, Jing Qu, Guang-Hui Liu & Weiqi Zhang

  6. Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, China

    Xuebao Wang, Shuai Ma, Shuhui Sun, Yandong Zheng, Chengyu Liu, Jing Qu & Guang-Hui Liu

  7. State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, China

    Shuai Ma, Shuhui Sun & Guang-Hui Liu

  8. Division of Endocrinology, Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou, China

    Feng Zhang & Qiaocheng Zhai

  9. The Joint Innovation Center for Engineering in Medicine, Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou, China

    Feng Zhang, Qiaocheng Zhai & Jinlin Ye

  10. International Center for Aging and Cancer, Hainan Medical University, Haikou, China

    Yaobin Jing & Guang-Hui Liu

  11. Liver Transplant Center, Organ Transplant Center, West China Hospital of Sichuan University, Chengdu, China

    Gang Xu & Jiayin Yang

  12. Laboratory of Liver Transplantation, Key Laboratory of Transplant Engineering and Immunology, NHC, West China Hospital of Sichuan University, Chengdu, China

    Gang Xu & Jiayin Yang

  13. Aging Translational Medicine Center, International Center for Aging and Cancer, Xuanwu Hospital, Capital Medical University, Beijing, China

    Si Wang & Guang-Hui Liu

  14. Advanced Innovation Center for Human Brain Protection, National Clinical Research Center for Geriatric Disorders, Xuanwu Hospital Capital Medical University, Beijing, China

    Si Wang & Guang-Hui Liu

  15. The Fifth People’s Hospital of Chongqing, Chongqing, China

    Si Wang

  16. Aging Biomarker Consortium, Beijing, China

    Si Wang, Jing Qu, Guang-Hui Liu & Weiqi Zhang

  17. Altos Labs, San Diego, CA, USA

    Juan Carlos Izpisua Belmonte

Authors
  1. Qiaoran Wang
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  2. Xuebao Wang
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Contributions

W. Z., G.-H.L., and J.Q. designed and coordinated the studies. Q.W. performed the majority of experiments; X.W. performed the snRNA-seq and RNA-seq analyses. S.M. provided guidance on snRNA-seq and RNA-seq analyses. Q.W., X.W. and B.L. wrote the first draft of the manuscript, and B.L. wrote the revised draft of the manuscript. F.Z. and Q.Z. aided human plasma sample collection. Y.F. helped on immunohistochemical studies. Y.J. provided the guidance for isolating cell nuclei and prepared samples for snRNA-seq. Y.D., M.X., G.X. and J. Yang helped on experiments, such as western blot and in vitro experiments. J.L., Y.Z. and C.L. helped on snRNA-seq analysis. S.S., J. Ye, S.W. and J.C.I.B. supervised the project. All authors contributed critical review of the paper.

Corresponding authors

Correspondence to Jing Qu, Guang-Hui Liu or Weiqi Zhang.

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Competing interests

The authors declare no competing interests.

Peer review

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Nature Aging thanks Antonio Lerario and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Phenotypic features underlying adrenal aging in cynomolgus monkeys.

(a) Dot plots showing the correlations between DHEA-S, aldosterone, cortisol, and age in human plasma, respectively (men, n = 141; women, n = 111). The color of dot indicates the sex of plasma-derived volunteers. (b) Plasma adrenal hormone levels (DHEA-S, aldosterone, cortisol) in young (4–6 years, n = 17), and elderly (16–18 years, n = 17) cynomolgus macaques. (c) Representative image, weight, and adrenal gland/body ratio of the intact adrenal gland tissues from young (n = 12) and old (n = 16) cynomolgus monkeys. (d) IHC staining for p21Cip1 in cynomolgus monkey adrenal tissues, representative images of tissue patterns (left) and quantification (right) are shown. The percentage of p21Cip1-positive cells is normalized to the young group as the reference for calculating fold change. Young, 4-5 years, n = 12; middle-aged, 10–12 years, n = 6; and elderly, 16–18 years, n = 17. (e) IHC staining for Aβ1-40 in cynomolgus monkey adrenal tissues, representative images of tissue patterns (left) and quantification (right) are shown. Aβ1-40-positive area is normalized to the young group as the reference for calculating fold change. Young, n = 12; middle-aged, n = 6; and elderly, n = 17. (f) Sudan black staining of cynomolgus monkey adrenal tissues (Young, n = 7; old, n = 8 monkeys). For bar graphs, data are presented as mean ± s.e.m. Statistical significance is determined in b, c, P value by two-tailed unpaired Student’s t-test, in d-f using a two-tailed non-parametric Mann-Whitney U test. c, scale bars, 1 cm. d-f scale bar = 20 µm.

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Extended Data Fig. 2 Molecular features of adrenal aging in cynomolgus monkeys.

(a) Bar plot showing the number of cells across different samples. Line charts showing the fraction of reads mapped to genome across different samples. (b) Box plots showing the distribution of nUMI (left) and gene number (right) per cell across different samples. (c) Scatter plot showing the Spearman correlation between different cell types. The size and color of dots represent the correlation between different cell types. (d) Stacked bar plot showing the cell type proportions in the young and old groups. (e) IHC staining for CYB5A in cynomolgus monkey adrenal tissues (Young, n = 12; old, n = 17 monkeys). Representative images of tissue patterns for the whole and zoom (left) and quantification (right) are shown. The dashed lines indicate the boundary of the ZG, ZF, ZR and M, respectively. Width ratio of CYB5A-positive area/cortex is normalized to the young group as the reference for calculating fold change. For bar graphs, data are presented as mean ± s.e.m. Statistical significance is determined in e using a two-tailed non-parametric Mann-Whitney U test. e, scale bar = 50 µm. For boxplots, the lower and upper hinges represent the first and third quartiles, the horizontal line in the box is the median, and the whiskers extend from the hinge to the largest (smallest) value no further than 1.5 × inter-quartile range from the hinge. Data beyond the end of the whiskers are categorized as ‘outlying’ points.

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Extended Data Fig. 3 Transcriptional fluctuations in aged NHP adrenal glands.

(a) Curve charts and dot plots showing the expression of the representative DEGs across different clusters along the pseudotime trajectory. (b) Box plots showing the gene set scores of representative pathways across different clusters in young and aged groups during regeneration of adrenocortical cells. (c) Dot plots showing the correlation between the cell identity score and transcriptional noise in the young and aged monkey adrenal glands. (d) Dot plots showing the correlation between the average gene expression level of the transcription noise correlated genes and transcriptional noise in ZR between old and young samples.

Extended Data Fig. 4 Molecular signatures underpinning NHP adrenal gland aging.

(a) Heatmap showing the aging-related DEGs included in Aging Atlas database. (b) Bar plots showing representative pathways of upregulated (left) and downregulated (right) DEGs included in Aging Atlas database. (c) Heatmap showing expression of upregulated DEGs in bulk RNA-seq across different cell types of young and aged groups. Gene expression levels across different DEGs are Z-score transformed. (d) Box plot showing the gene set scores of CREM target genes across different cell types in young and aged groups. (e) Bar plot showing representative pathways of CREM target genes.

Extended Data Fig. 5 Chronic inflammation within NHP adrenal aging.

(a) IHC staining for CD45 in cynomolgus monkey adrenal tissues, representative images of tissue patterns for the whole and zoom (left) and quantification (right) are shown. The percentage of CD45-positive cells is quantified as fold changes (old vs. young). Young, n = 7; and elderly, n = 8. (b) IHC staining for CD3 in cynomolgus monkey adrenal tissues, representative images of tissue patterns for the whole and zoom (left) and quantification (right) are shown. The percentage of CD3-positive cells is quantified as fold changes (old vs. young). Young, n = 7; elderly, n = 8. (c) IHC staining for CD163 in cynomolgus monkey adrenal tissues, representative images of tissue patterns for the whole and zoom (left) and quantification (right) are shown. The percentage of CD163-positive cells is quantified as fold changes (old vs. young). Young, n = 7; elderly, n = 8. (d) Dot plot showing the age-specific cell–cell interaction pairs in indicated cell types between young and aged groups. The font colors of cell interactions correspond to different age groups. Blue represents young-specific cell pairs, while red represents old-specific cell pairs. The size of the dots represents the -Log10 P value and the color of dots indicates the mean value of expression levels. (e) Bar plot showing representative pathways of upregulated (up) and downregulated (bottom) DEGs across different cell types with aging. (f) Left, upset map showing the distribution of common and unique upregulated aging-related DEGs among different adrenocortical cell types. Right, heatmaps showing the representative pathways of DEGs across different modules. For bar graphs, data are presented as mean ± s.e.m. Statistical significance is determined in a-c, P value by two-tailed non-parametric Mann-Whitney test. a-c, the dashed lines indicate the boundary of the ZG, ZF, ZR and M, respectively. a-c, scale bar = 20 µm.

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Extended Data Fig. 6 Reduced LDLR hinders DHEA-S secretion in adrenocortical cells.

(a) Western blot images (left) and quantification (right) showing LDLR protein level in young (n = 4), middle-aged (n = 3) and old (n = 4) cynomolgus monkey adrenal gland tissues. (b) GSEA enrichment curves show no significant change in the aging-related pathway after LDLR knockout in NCI-H295R cell. (c) Volcano plot showing the results of differential expression analysis by bulk RNA-seq in NCI-H295R cells transfected with CRISPRko sg-NTC and sg-LDLR lentivirus. Each dot represents a gene. For the western blot analysis, in a, GAPDH is used as the loading control. For bar graphs, data are presented as mean ± s.e.m. b, c, n = 4 biological replicates. Statistical significance is determined in a, P value by two-tailed unpaired Student’s t-test.

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Supplementary information

Supplementary Information

Supplementary methods, Fig. 1 (the uncropped blots) and Fig. 2 (examples of gating strategy) and the full legends for Supplementary Tables 1–12.

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Supplementary Tables 1–12

Legends and descriptive captions of all the supplementary tables listed in Supplementary Information.

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Wang, Q., Wang, X., Liu, B. et al. Aging induces region-specific dysregulation of hormone synthesis in the primate adrenal gland. Nat Aging 4, 396–413 (2024). https://doi.org/10.1038/s43587-024-00588-1

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