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

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PHENYLACETIC ACID INDUCES ENDOTHELIAL CELL SENESCENCE DURING AGING Article Open access 12 May 2025 SGLT2 INHIBITION ELIMINATES SENESCENT CELLS AND ALLEVIATES PATHOLOGICAL AGING Article Open

access 30 May 2024 CELLULAR SENESCENCE AND ITS ROLE IN WHITE ADIPOSE TISSUE Article 28 January 2021 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

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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 * These authors contributed equally: Hao Yang, Tongyao Wang,

Chenglang Qian, Huijing Wang, Dong Yu, Meifang Shi. AUTHORS AND AFFILIATIONS * 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 * Institute of Wound Prevention and Treatment, Shanghai

University of Medicine and Health Sciences, Shanghai, China Huijing Wang * Department of Precision Medicine, Translational Medicine Research Center, Naval Medical University and Shanghai Key

Laboratory of Cell Engineering, Shanghai, China Dong Yu * Department of Clinical Laboratory, Youyi Road Community Health Service Centre for Baoshan District, Shanghai, China Meifang Shi 

& Tao Liu * Department of Pathology, School of Basic Medical Sciences, Shanghai Medical College, Fudan University, Shanghai, China Xueguang Liu * Department of Medical Chemistry,

Graduate School of Medicine, Kyoto University, Kyoto, Japan Xingyu Rong * Department of Physiology and Pathophysiology, School of Basic Medical Sciences, Shanghai Medical College, Fudan

University, Shanghai, China Yufan Zheng, Hui Yang & Ning Sun * Shanghai Key Laboratory of Clinical Geriatric Medicine, Huadong Hospital, Fudan University, Shanghai, China Yiqin Huang 

& Chao Zhao * 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 * 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 * Wuxi School of Medicine, Jiangnan University, Jiangsu, China Ning Sun * Engineering Research Center of Intelligent

Healthcare for Successful Aging, Ministry of Education, Fudan University, Shanghai, China Chao Zhao Authors * Hao Yang View author publications You can also search for this author inPubMed 

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author inPubMed Google Scholar * Chao Zhao View author publications You can also search for this author inPubMed Google Scholar 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. ETHICS DECLARATIONS COMPETING INTERESTS The authors declare no competing interests. PEER

REVIEW PEER REVIEW INFORMATION _Nature Aging_ thanks Tohru Minamino and the other, anonymous, reviewer(s) for their contribution to the peer review of this work ADDITIONAL INFORMATION

PUBLISHER’S NOTE Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. 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).

Source data 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). Source data 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). Source data 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). Source data 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). Source data 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) Source data SUPPLEMENTARY INFORMATION SUPPLEMENTARY INFORMATION REPORTING SUMMARY SOURCE DATA SOURCE DATA FIG. 1 Statistical source data for Fig. 1. SOURCE DATA FIG. 3

Statistical source data for Fig. 3. SOURCE DATA FIG. 4 Statistical source data, unprocessed western blots and unprocessed images for Fig. 4. SOURCE DATA FIG. 5 Statistical source data,

unprocessed western blots and unprocessed images for Fig. 5. SOURCE DATA FIG. 6 Statistical source data and unprocessed western blots for Fig. 6. SOURCE DATA FIG. 7 Statistical source data

and unprocessed western blots for Fig. 7. SOURCE DATA EXTENDED DATA FIG. 1 Statistical source data for Extended Data Fig. 1. SOURCE DATA EXTENDED DATA FIG. 2 Statistical source data for

Extended Data Fig. 2. SOURCE DATA EXTENDED DATA FIG. 3 Statistical source data for Extended Data Fig. 3. SOURCE DATA EXTENDED DATA FIG. 4 Statistical source data, unprocessed western blots

and unprocessed images for Extended Data Fig. 4. SOURCE DATA EXTENDED DATA FIG. 5 Statistical source data and unprocessed images for Extended Data Fig. 5. SOURCE DATA EXTENDED DATA FIG. 6

Statistical source data, unprocessed western blots and unprocessed images for Extended Data Fig. 6. SOURCE DATA EXTENDED DATA FIG. 7 Statistical source data, unprocessed western blots and

unprocessed images for Extended Data Fig. 7. SOURCE DATA EXTENDED DATA FIG. 8 Statistical source data and unprocessed western blots for Extended Data Fig. 8. SOURCE DATA EXTENDED DATA FIG. 9

Statistical source data and unprocessed western blots for Extended Data Fig. 9. RIGHTS AND PERMISSIONS Springer Nature or its licensor (e.g. a society or other partner) holds exclusive

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by the terms of such publishing agreement and applicable law. Reprints and permissions ABOUT THIS ARTICLE CITE THIS ARTICLE 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 Download citation * Received: 21 November 2023 *

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