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Gut microbial-derived phenylacetylglutamine accelerates host cellular senescence

Nature Aging volume 5, pages 401–418 (2025)Cite this article

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Abstract

Gut microbiota plays a crucial role in the host health in the aging process. However, the mechanisms for how gut microbiota triggers cellular senescence and the consequent impact on human aging remain enigmatic. Here we show that phenylacetylglutamine (PAGln), a metabolite linked to gut microbiota, drives host cellular senescence. Our findings indicate that the gut microbiota alters with age, which leads to increased production of phenylacetic acid (PAA) and its downstream metabolite PAGln in older individuals. The PAGln-induced senescent phenotype was verified in both cellular models and mouse models. Further experiments revealed that PAGln induces mitochondrial dysfunction and DNA damage via adrenoreceptor (ADR)–AMP-activated protein kinase (AMPK) signaling. Blockade of ADRs as well as senolytics therapy impede PAGln-induced cellular senescence in vivo, implying potential anti-aging therapies. This combined evidence reveals that PAGln, a naturally occurring metabolite of human gut microbiota, mechanistically accelerates host cellular senescence.

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Fig. 1: Gut microbiota–host co-metabolite PAGln is age associated.
Fig. 2: Gut microbial features in older link to plasma PAA and PAGln.
Fig. 3: Microbiota in the aged have stronger PAA-producting ability.
Fig. 4: PAGln triggers cellular senescence both in vivo and in vitro.
Fig. 5: PAGln induces mitochondrial dysfunction and fragmentation.
Fig. 6: PAGln induces mito-dysfunction via the ADR–AMPK pathway.
Fig. 7: ADR blockade and senolytics attenuate cellular senescence.

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Data availability

All data supporting the findings of this study are available within the paper and its Supplementary Information files. All RNA-seq data can be viewed in the National Genomics Data Center (NGDC) Human-GSA (https://ngdc.cncb.ac.cn/gsa-human/browse/) by accession number HRA008330. Metagenome data for microbiome have been deposited in the NGDC-GSA (https://ngdc.cncb.ac.cn/gsa/) and can be accessed by CRA006219. The scRNA-seq data were deposited in the NGDC-OMIX (https://ngdc.cncb.ac.cn/omix/releaseList) and can be accessed by OMIX007169. The public RNA-seq dataset of HUVECs used in this study can be accessed in the European Nucleotide Archive (https://www.ebi.ac.uk/ena/browser/search) via accession number PRJEB34248. Japanese Multi Omics Reference Panel (https://jmorp.megabank.tohoku.ac.jp/), Ensembl (https://www.ensembl.org/index.html), KEGG (https://www.genome.jp/kegg/), GO (https://www.geneontology.org/), UniProt (https://www.uniprot.org/) and CellMarker database (http://xteam.xbio.top/CellMarker/) were used in the study. Any other data reported in this paper are available from the lead contact upon reasonable request.

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Acknowledgements

This work was funded with the following grants: Program of Shanghai Academic Leader for Health (2022XD051 to C.Z.); the National Key Research and Development Project of China (2018YFC2000200 to N.S. and 2018YFC2000500 to C.Z.); the National Clinical Research Center (2023KF2004 to C.Z.); the Shanghai Municipal Science and Technology Major Project (ZD2021CY001 to N.S.); and the Innovation of Fudan University (2021 to C.Z.). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. We thank Z. Zou, H. Yu, H. Gu and the staff of the Youyi Road Community Health Service Centre in Shanghai for their assistance with sample collection and clinical information collection. We thank Q. Liu for his invaluable comments, X. Zhao of Vanke School Pudong and A. Devlin of Science Editing Experts for English editing and proofreading. We acknowledge that Figs. 1a, 3g and 7a,c, Extended Data Fig. 6a and Supplementary Fig. 1 in this paper were created with BioRender.

Author information

Author notes

  1. These authors contributed equally: Hao Yang, Tongyao Wang, Chenglang Qian, Huijing Wang, Dong Yu, Meifang Shi.

Authors and Affiliations

  1. National Clinical Research Center for Aging and Medicine, Huashan Hospital and MOE/NHC/CAMS Key Laboratory of Medical Molecular Virology, School of Basic Medical Sciences, Shanghai Medical College, Fudan University, Shanghai, China

    Hao Yang, Tongyao Wang, Chenglang Qian, Mengwei Fu, Miaomiao Pan, Zhenming Xiao, Xiejiu Chen, Anaguli Yeerken, Yonglin Wu, Ming Zhang, Peng Qiao, Yifan Qu, Yong Lin, Yumei Wen & Chao Zhao

  2. Institute of Wound Prevention and Treatment, Shanghai University of Medicine and Health Sciences, Shanghai, China

    Huijing Wang

  3. Department of Precision Medicine, Translational Medicine Research Center, Naval Medical University and Shanghai Key Laboratory of Cell Engineering, Shanghai, China

    Dong Yu

  4. Department of Clinical Laboratory, Youyi Road Community Health Service Centre for Baoshan District, Shanghai, China

    Meifang Shi & Tao Liu

  5. Department of Pathology, School of Basic Medical Sciences, Shanghai Medical College, Fudan University, Shanghai, China

    Xueguang Liu

  6. Department of Medical Chemistry, Graduate School of Medicine, Kyoto University, Kyoto, Japan

    Xingyu Rong

  7. Department of Physiology and Pathophysiology, School of Basic Medical Sciences, Shanghai Medical College, Fudan University, Shanghai, China

    Yufan Zheng, Hui Yang & Ning Sun

  8. Shanghai Key Laboratory of Clinical Geriatric Medicine, Huadong Hospital, Fudan University, Shanghai, China

    Yiqin Huang & Chao Zhao

  9. Department of Human Anatomy, Research Centre for Bone and Stem Cells; Key Laboratory for Aging and Disease; The State Key Laboratory of Reproductive Medicine; Nanjing Medical University, Nanjing, China

    Jianliang Jin

  10. Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences and Shanghai Key Laboratory of Aging Studies, Pudong, Shanghai, China

    Nan Liu

  11. Wuxi School of Medicine, Jiangnan University, Jiangsu, China

    Ning Sun

  12. Engineering Research Center of Intelligent Healthcare for Successful Aging, Ministry of Education, Fudan University, Shanghai, China

    Chao Zhao

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  1. Hao Yang
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  2. Tongyao Wang
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Contributions

C.Z., H.W. and Hao Yang conceptualized the study. C.Z., N.S., Hao Yang, T.W., C.Q., H.W., D.Y. and M.S. designed the study. Hao Yang, T.W., C.Q., H.W., D.Y., H.W., M.S., M.F., X.L., M.P., X.R., Z.X., X.C., A.Y., Y.W., Y.Z., Hui Yang, M.Z., T.L., P.Q., Y.Q., Y.L, Y.H., J.J., N.L. and C.Z. collected the samples and performed the experiments. Hao Yang, T.W., C.Q., H.W., D.Y., X.R., X.C., Y.H. and C.Z. contributed to data analysis. Hao Yang, T.W., C.Q., H.W., D.Y., N.S., Y.W. and C.Z. wrote the paper collaboratively. C.Z. and N.S. acquired funding and supervised the study. All authors critically revised the draft and approved the final paper.

Corresponding authors

Correspondence to Ning Sun or Chao Zhao.

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Extended data

Extended Data Fig. 1 Gut microbiota-host co-metabolite PAGln is age-associated.

(a-b) Correlation between the plasma phenylacetic acid concentration and chronological age in the healthy discovery (a) and validation (b) populations. Pearson’s correlation coefficient (r) values were displayed. The semi-log regression fit curves (black lines) and 95% confidence intervals (pink areas) are shown. (c) Plasma phenylacetylglutamine (ToMMo compound ID: TCI005603) levels in each chronological age groups (20 s to 80 s~) from the Japanese Multi Omics Reference Panel (n = 2957 individuals). The boxes in blue and red represent males and females, respectively. Data were shown as median with min and max. The median lines within the box: median values; boxes: lower and upper quartiles; top whiskers: the max (Q3 + 1.5*IQR); bottom whiskers: the min (Q1-1.5*IQR). Isolated points: outliers. Statistical analysis was performed using the Kruskal-Wallis test. (d) Random forest model based on shared differential metabolites of the discovery dataset, estimated by the cross-validation. The out-of-bag (OOB) (upper right) error rate using 5 metabolites is shown. (e) ROC curves for the random forest model based on minimal metabolites of the discovery dataset. The area under the curve (AUC) values are provided. (f, g) ROC curves for the random forest model based on shared differential (f) and minimal (g) metabolites of the validation dataset. The area under the curve (AUC) values are provided. (h, i) Bar (h) and scatter (i) plots display the top 5 metabolites in random forest model for the discovery dataset based on the shared differential and minimal metabolites, respectively. (j-k) Scatter (j and l) and spline (k and m) plot of the correlation between adenosine monophosphate (AMP) (j and k) and maltose/lactose ratio (l and m) concentration and chronological age in the healthy discovery population. (n) Correlation between the plasma PAGln levels normalized to PAA and chronological age in the healthy validation cohorts. Spearman’s correlation coefficient (Rho) values were displayed. The fit spline (black lines) is shown. a, d-e, h-m n = 132 individuals for the healthy discovery population; b, f-g, n n = 80 individuals for the healthy validation cohort; a-b, n Correlation coefficients and P-values were calculated using two-tailed Pearson correlation (a and b), Spearman rank correlation (n) analysis.

Source data

Extended Data Fig. 2 Gut microbial features in older link to plasma PAA and PAGln.

(a) Principal components analysis (PCA) plot of individual gut microbiota. (b) Differences of gut microbiota composition (measured by Bray-Curtis index) among age groups. (c) Differences of alpha diversity (measured by Shannon index) of gut microbiota among age groups. (d, e) Bar plot of the standardized relative abundance of PWY-5100 (pyruvate fermentation to acetate and lactate II) (d) and PWY-6628 (super pathway of L-phenylalanine biosynthesis) (e). Vertical dotted lines represent the age cutoffs (60 and 80 years). (f) Network diagram depicting interactions between PAGln, PAA, young-groups-enriched bacterial species, and pathways. Red lines connect elements with a positive correlation (adjusted P < 0.05), while blue lines connect elements with a negative correlation (adjusted P < 0.05). Line width corresponds to correlation coefficient. Elements in rectangles represent bacterial pathways, ovals represent bacterial species, and red diamonds represent metabolite. Hub nodes in the network are framed with black rectangles. (g) Network diagram depicting interactions among PAGln, PAA, old-group-enriched bacterial species, and pathways. Red lines connect elements with a positive correlation (adjusted P < 0.05), while blue lines connect elements with a negative correlation (adjusted P < 0.05). Line width corresponds to correlation coefficient. Elements in rectangles represent bacterial pathways, ovals represent bacterial species, and red diamonds represent metabolite. Hub nodes in the network are framed with black rectangles. a-g Young: n = 64 individuals; Old: n = 32 individuals; Older: n = 36 individuals for health discovery cohort; P values of the correlation coefficients were calculated using two-sided Spearman rank test with Benjamini-Hochberg (BH) adjustment (f, g) and Wilcoxon rank test (b, c).

Source data

Extended Data Fig. 3 Microbiota in the aged have stronger PAA-producting ability.

(a) Age difference of individuals in Q1 (n = 33 individuals) and Q4 (n = 33 individuals) based on plasma PAGln concentration. Data are shown as median with min-max. The median lines within the box represent the median values, while the boxes represent lower and upper quartiles. The top end points of the whiskers represent the maximum value and the bottom end points of the whiskers represent the minimum value. (b-d) Age difference of individuals in Q1 and Q4 whose fecal samples underwent qPCR relative quantification of porA (b, Q1 n = 23 individuals; Q4 n = 29 individuals), BT04029 (c, Q1 n = 25 individuals; Q4 n = 19 individuals), and BT04030 (d, Q1 n = 25 individuals; Q4 n = 19 individuals). Each dot represents an individual. The median lines represent the mean values. (e) Age difference of younger and older individuals whose fecal samples were tested for PAA-producing ability (n = 14 individuals each group). Each dot represents an individual. Orange line represents mean value. (f-h) Relative abundance of Methanobrevibacter smithii (f), Lactonifactor longoviformis (g), and Clostridium methylpentosum (h) in fecal samples from Young, Old, and Older (Young: n = 64 individuals; Old: n = 32 individuals; Older: n = 36 individuals for health discovery cohort) populations. Each dot represents an individual. (i) Phylogenetic chart of 56 bacterial strains successfully isolated from older individuals’ fecal samples. Gordonibacter pamelaeae and Clostridium scindens, two of the bacteria predicted to produce PAA from L-Phe and enriched in fecal samples from older individuals, are marked in red. Statistical analysis was performed using Kruskal-Wallis test with Dunn’s multiple comparisons test (f-h) and two-tailed Student’s t-test (a-e).

Source data

Extended Data Fig. 4 PAGln triggers cellular senescence both in vivo and in vitro.

(a) Growth curve analysis in IMR-90 under administration of different doses of PAGln, including withdrawal of PAGln. (b) EdU staining analysis and quantification of EdU+ cells in IMR-90. Scale bar, 50 μm. (c-e) Ki-67 staining analysis and quantificantion of Ki-67+ cells in HUVEC (c, left panel and d) and IMR-90 (c, right panel and e). Scale bar, 50 μm. (f) Relative gene expression analysis of SASP genes in IMR-90 receiving different doses of PAGln. Data were obtained by qRT-PCR. Colors ranging from blue to red represent decreased to increased gene expression relative to cells from control group (0 μM PAGln). (g) Representative images and quantification of immunoblotting for cell senescence markers and DNA damage markers in lungs from mice receiving PAGln administration or vehicle (saline) administration, with β-actin as endogenous control. (h-k) Representative images and quantification of p16 immunofluorescence in kidneys (h and j) and lungs (i and k) from p16-/-, saline- and PAGln- treated mice. White box indicates ROI. Scale bars, 20 μm. b-e, h-k n = 6 biological replicates; a, f n = 3 biological replicates; g 8-week-old, male, n = 6 mice per group; a, b, d, e, g, j, k Data were shown as mean ± s.d. Statistical analysis was performed using one-way ANOVA with Bonferroni’s test (a, b right, d, and e) and two-sided t test (g right, j and k).

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Extended Data Fig. 5 PAGln triggers major cell senescence in kidneys and lungs.

(a) Relative gene expression analysis of cell senescence genes and SASP genes in lungs from mice receiving PAGln or saline administration. Data were obtained by qRT-PCR. Colors ranging from blue to red represent decreased to increased genes expression relative to those of vehicle group. (b) Heatmap showing the SASP positive percentage of the two major cell types (data from scRNA-seq) of kidneys from saline- and PAGln- treated mice. (c) Representative immunofluorescence (confocal) images and quantification analysis of the p21 expression in CD31+ (top panel) and F4/80+ (bottom panel) cells from the kidneys of the saline- and PAGln- treated mice. Scare bars, 20 μm. (d) UMAP plots of cell types (left panel), cells expression Cdkn1a (middle two panel, shown as purple dots) in lungs from saline- and PAGln- treated mice. The heatmap of Cdkn1a positive percentage of each cell type (relative to saline group) in lungs were shown (right panel). (e) Heatmap showing the SASP positive percentage of the two major cell types (data from scRNA-seq) of lungs from saline- and PAGln- treated mice. (f) Representative immunofluorescence (confocal) images and quantification analysis of the p21 expression in CD31+ (top panel) and Vimentin+ (bottom panel) cells from the lungs of the saline- and PAGln- treated mice. Scare bars, 20 μm. a, c, f 8-week-old, male, n = 6 mice per group. c (right), f (right) Data were shown as mean ± s.d. Statistical analysis was performed using two-sided t test (c right, f right).

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Extended Data Fig. 6 G. pamelaeae accelerates cellular senescence in vivo.

(a) Schematic figures of G. pamelaeae transplantation experiments in the ABX-challenged SPF mice (left panel) and germ-free (GF) mice (right panel). (b) Serum PAGly and fecal absolute abundance of G. pamelaeae in ABX-challenged mice during the G. pamelaeae transplantation (Day7 and Day14) were detected. (c) Serum PAGly and fecal absolute abundance of G. pamelaeae in GF mice during the G. pamelaeae transplantation (Day0 and Day14) were detected. (d, e) Representative images (left panel) and quantification (bottom right panel) of immunoblots for cellular senescence markers in the kidneys (p) and lungs (q) of SPF mice treated with vehicle or G. pamelaeae. The expression of Cdkn2a, Cdkn1a, Trp53, and SASP in the kidneys (p) and lungs (q) of these mice was analyzed by qPCR (top right panel). (f, g) Representative images (left panel) and quantification (middle panel) (8-week-old, male, n = 4 mice per group) of immunoblots for cellular senescence markers in the kidneys (f) and lungs (g) of GF mice treated with vehicle or G. pamelaeae. The expression of Cdkn2a, Cdkn1a, Trp53, and SASP in the kidneys (r) and lungs (s) of these mice was analyzed by qPCR (right panel). (h) Renal function was assessed by serum creatinine, blood urea nitrogen, and uric acid levels in mice treated with saline or PAGln. (i) Representative images and quantification analysis of Periodic Acid Schiff staining (PAS) of glomeruli (left and right upper panel) and Masson trichrome staining of lungs (middle and right lower panel) in mice from saline and PAGln groups. b, d, e, h, i 8-week-old, male, n = 6 mice per group; c, f, g 8-week-old, male, n = 4 mice per group. Data were shown as mean ± s.d. (b, c, d right lower panel, e right lower panel, f middle panel, g middle panel, h, and i right panels). Statistical analysis was performed using two-sided t test (the first and third panel of b, the left two panel of c, d right lower panel, e right lower panel, f middle panel, g middle panel, h, and i right panels), and two-sided Mann-Whitney test (the second and forth panel of b, and the third panel of c).

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Extended Data Fig. 7 PAGln induces mitochondrial dysfunction and fragmentation.

(a) Gating strategy for cells (HUVEC and IMR-90) in flow cytometry to detect mitochondrial function (left panel). Representative TMRE fluorescence peak graph and quantification of mitochondrial membrane potential using TMRE in IMR-90 cells under different doses of PAGln administration were provided; MFI: mean fluorescence intensity. (b) Quantification analysis of cellular ROS using DCFH-DA in IMR-90 cells under different doses of PAGln administration, data were calculated by flow cytometry; MFI: mean fluorescence intensity. (c) Representative confocal images of Tom20-labeled mitochondria in IMR-90 cells under different doses of PAGln administration. Scale bar, 10 μm. This experiment was repeated independently for three times with similar results. (d) Quantitative analysis of the proportion of cells with fragmented mitochondria (left) and mitochondria length (right) based on TOM20 staining in IMR-90 cells under different doses of PAGln administration. (e) Representative images and quantification of immunoblotting for mito-fusion markers (OPA1, MFN1, MFN2), mito-fission markers (p-DRP1/DRP1, MFF), and DNA damage markers (γ-H2AX/H2AX) in IMR-90 cells under 24-hour administration of different doses of PAGln. a, b right, and d n = 6 biological replicates; e n = 3 biological replicates; d left 0 µM: n = 1490 mitochondria, 5 µM: n = 1384 mitochondria, 15 µM: n = 990 mitochondria, 30 µM: n = 770 mitochondria. a right panel, b, d left panel, and e right panel Data were shown as mean ± s.d. Statistical analysis was performed using one-way ANOVA with Bonferroni’s test (a right, b, d left, and e right), and Kruskal-Wallis rank test with Dunn’s multiple comparisons (d right).

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Extended Data Fig. 8 PAGln induces mito-dysfunction via ADR-AMPK pathway.

(a) Representative images and quantification of immunoblotting for p-DRP1/DRP1 and p-AMPK/AMPK, with Vinculin as an endogenous control, in IMR-90 cells under 1-hour administration of different doses of PAGln. (b) Representative images and quantification of immunoblotting for p-DRP1/DRP1 and p-AMPK/AMPK, with Vinculin as an endogenous control, in IMR-90 cells under 1-hour administration of PAGln (15 μM) and/or Compound C (1 μM), an established AMPK inhibitor. (c) Representative images and quantification of immunoblotting for mito-fission markers (p-DRP1/DRP1, MFF), DNA damage markers (γ-H2AX/H2AX), and p-AMPK/AMPK in IMR-90 cells under 24-hour administration of PAGln (15 μM) and Compound C (1 μM), respectively or combined. (d) Representative images and quantification of immunoblotting of mito-fusion (MFN1, MFN2), mito-fission (p-DRP1/DRP1, MFF), redox balance (SOD1 and SOD2), and AMPK signaling (p-AMPK/AMPK) markers from the lungs of saline- and PAGln- treated mice. (e) Representative images and quantification of immunoblotting for p-DRP1/DRP1 and p-AMPK/AMPK, with Vinculin as an endogenous control, in IMR-90 cells under 1-hour administration of PAGln (15 μM) with or without phentolamine (1 μM) and propranolol (1 μM), respectively. (f) Representative images and quantification of immunoblotting for mito-fission marker (p-DRP1/DRP1) and DNA damage marker (γ-H2AX/H2AX) in IMR-90 cells under 24-hour administration of PAGln with or without phentolamine (1 μM) and propranolol (1 μM), respectively. (g) Representative images and quantification of immunoblotting for mito-fission markers (p-DRP1/DRP1, MFF), DNA damage marker (γ-H2AX/H2AX), and p-AMPK/AMPK in HUVECs under 24-hour administration of PAGln (15 μM) and carvedilol (1 μM), respectively or combined. (h) The expression of adrenoreceptors in HUVECs from public RNA-seq data. (i) Representative images and quantification of immunoblotting for p-DRP1/DRP1, p-AMPK/AMPK, γ-H2AX/H2AX, and three adrenoreceptors (ADRA2B, ADRB1, and ADRB2) in HUVECs receiving 0 or 15 μM PAGln after genetic intervention of adrenoreceptors via shRNA. shRNAs of two different sequences were designed and examined for each adrenoreceptor to avoid off-target effects. a-c, e-i n = 3 biological replicates; d 8-week-old, male, n = 6 mice per group. a-i Data were shown as mean ± s.d. Statistical analysis was performed using one-way ANOVA with Bonferroni’s test (a, b, c, e, f, g, i), and two-tailed Student’s t-test (d).

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Extended Data Fig. 9 ADR blockade and senolytics attenuate cellular senescence.

(a) Representative images and quantification of cell senescence markers (p53, p21, p16, and p-Rb/Rb) in HUVECs under administration of PAGln (15 μM) and carvedilol (1 μM), respectively or combined. (b) Representative images and quantification of immunoblotting for cell senescence markers (p-Rb/Rb, p53, p21, p16, and Lamin B1) and DNA damage marker (γ-H2AX/H2AX) in the lungs of mice receiving PAGln and carvedilol, respectively or combined. (c, d) Relative gene expression analysis of cell senescence and SASP genes in the kidneys (c) and lungs (d) of mice receiving PAGln and carvedilol, respectively or combined. Data were obtained by qRT-PCR. Colors ranging from blue to red represent decreasing to increasing gene expression relative to kidney (c) or lung (d) samples from the control group (vehicle only). (e) Representative images and quantification of immunoblotting for cell senescence markers (p-Rb/Rb, p53, p21, p16, and Lamin B1) and DNA damage marker (γ-H2AX/H2AX) in the lungs of mice receiving PAGln and ABT-263, respectively or combined. (f, g) Relative gene expression analysis of cell senescence and SASP genes in the kidneys (f) and lungs (g) of mice receiving PAGln and carvedilol, respectively or combined. Data were obtained by qRT-PCR. Colors ranging from blue to red represent decreasing to increasing gene expression relative to kidney (f) or lung (g) samples from the control group (vehicle only). a n = 3 biological replicates; b, e 8-week-old, male, n = 6 mice per group. a, b, and e Data were shown as mean ± s.d. Statistical analysis was performed using one-way ANOVA with Bonferroni’s test (a, b, and e, except representative images)

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Yang, H., Wang, T., Qian, C. et al. Gut microbial-derived phenylacetylglutamine accelerates host cellular senescence. Nat Aging 5, 401–418 (2025). https://doi.org/10.1038/s43587-024-00795-w

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