
==== Front
Redox Biol
Redox Biol
Redox Biology
2213-2317
Elsevier

S2213-2317(24)00273-8
10.1016/j.redox.2024.103295
103295
Research Paper
Combinatorial lipidomics and proteomics underscore erythrocyte lipid membrane aberrations in the development of adverse cardio-cerebrovascular complications in maintenance hemodialysis patients
Zheng Ke a1
Qian Yujun ab1
Wang Haiyun a
Song Dan a
You Hui c
Hou Bo c
Han Fei d
Zhu Yicheng d
Feng Feng c
Lam Sin Man smlam@lipidall.com
e⁎⁎1
Shui Guanghou ghshui@genetics.ac.cn
e⁎2
Li Xuemei lixm@pumch.cn
a⁎⁎⁎2
a Department of Nephrology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
b Department of Nephrology, Jiangsu Province Hospital/The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
c Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
d Department of Neurology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
e State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China
⁎ Corresponding author. ghshui@genetics.ac.cn
⁎⁎ Corresponding author. smlam@lipidall.com
⁎⁎⁎ Corresponding author. lixm@pumch.cn
1 These authors have contributed equally to this work.

2 These authors have contributed equally to this work.

31 7 2024
10 2024
31 7 2024
76 10329519 6 2024
21 7 2024
31 7 2024
© 2024 The Authors
2024
https://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Age-associated deterioration of physiological functions occur at heterogeneous rates across individual organs. A granular evaluation of systemic metabolic mediators of aging in a healthy human cohort (n = 225) identified prominent increases in circulating uremic toxins that were recapitulated in mice, on which we further characterized the aging phenome across five peripheral organs. Our multi-omics analyses connected systemic aging profiles primarily to kidney metabolism, uncovering a metabolic association between localized glucosylceramide (GluCer) accretion and renal functional decline. Elevated GluCers were also associated with higher risk of deaths in an independent cohort of aged individuals (n = 271). We report GluCer-mTOR signaling commencing at late middle-age that disrupts mitophagy and undermines mitochondrial respiration in kidney. Conserved between human and mice, GluCer-mediated renal dysfunction is female-biased and modulated by intracellular purines. Our work provides molecular basis for the sexually disparate effects of mTOR inhibition on mammalian lifespan, possibly ascribed to the evolutionary cost of female reproduction.
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pmc1 Introduction

Medical advancements have significantly improved the quality of life and substantially extended the average human lifespan. Concurrently, aging-related diseases have emerged as major public health concerns. The United Nations projected that worldwide, the number of persons aged 65 years or above will reach 1.5 billion in 2050 [1]. In China, the percentage increase in older persons aged 65 years or over between 2019 and 2050 is estimated at 19.9 % [1], which greatly elevates the risk of various non-infectious, age-dependent chronic diseases such as cardiovascular disease (CVD), chronic obstructive pulmonary disease, chronic kidney disease (CKD), dementia and diabetes [2]. Aging represents the greatest common risk factor for chronic diseases [3], while age-related functional decline of different organs denote commonalities underlying various chronic diseases. The trajectory of age-related deterioration in function varies for individual organs, and biological aging clocks specific to distinct organ systems have been established [4]. Chronic diseases impact the overall senescence of body systems in disparate ways. For instance, patients with CKD were found to exhibit the oldest body ages amongst 16 categories of chronic diseases [4]. A multi-organ characterization of aging phenomes coupled with the identification of systemic metabolic mediators of aging is, therefore, expected to unravel inter-organ crosstalk that coordinates the overall aging process of the organism as an entity.

Lipids, as integral structural components of mammalian systems, partake in various biochemical signaling processes that underpin cellular and tissue function [5]. Preceding studies have implicated lipids in the pathogenesis of various age-related diseases such as CVD [6,7], diabetes [8,9], Alzheimer's disease (AD) [[10], [11], [12]] and CKD [13,14]. As examples, changes in ether lipids [10], including plasmalogen lipids [11], and ceramides (Cer) [10,12] have been reported in AD. Peripheral signatures from two large clinical studies of AD have identified ether lipids and sphingolipids to be associated with prevalent AD [10]. On the other hand, lower levels of lysophosphatidylcholines (LPCs) were associated with renal failure in CKD [13], while changes in phosphatidylcholines and triacylglycerols were correlated with higher CKD risk [14]. On top of lipids, polar metabolites constitute an added dimension of the biological phenome that together confer the closest readout of the cellular phenotype. Metabolomic profiling of aging cohorts has identified several metabolites from, for examples, the categories of carbohydrates, amino acids and nucleotides, which were associated with the aging phenotype [15,16]. Omics-driven approaches are useful in offering a granular view of the metabolic landscape that facilitates the discovery of novel functional metabolites under different biological context [17]. Analytical challenges such as limited metabolite identification and imprecise quantification, however, can substantially impede the broader application of metabolomics in aging research and undermine the validity of age-related metabolite markers [18]. In addition, age-associated metabolic perturbations in humans might not be fully recapitulated in mouse models [19], rendering a combinatorial approach necessary in deciphering age-related metabolic interventions to ameliorate pathological aging in humans.

In this study, we first utilized precise, quantitative metabolomic approaches [18] to establish an array of systemic, age-related metabolite alterations in the human plasma of a cross-sectional cohort. These systemic metabolic mediators were recapitulated during the aging process in mice, which enabled us to leverage on the murine model to uncover the contributory roles of different organ systems towards the systemic metabolic perturbations observed in human aging. Using high-coverage, quantitative lipidomic profiling [20] of metabolic changes in five major peripheral organs and/or tissues in mice, our findings untangle metabolic signaling pathways crucial to preserving kidney function, which contribute positively to human health and unveil new intervention targets to promote healthy aging.

2 Results

2.1 Overlapping metabolic features between human systemic aging and compromised renal function

Using a robust, non-targeted strategy for accurate quantitation and precise profiling of 480 metabolites developed in-house [18], we investigated the aging-associated plasma metabolomes in a cross-sectional cohort of 225 ostensibly healthy individuals aged between 20 and 88 years (Table 1). Linear regression analysis uncovered an array of 280 plasma metabolites associated with aging. Over 70 % (200/280) of these age-associated metabolites were positively correlated with increasing age, which predominantly consisted of acylcarnitines, long-chain fatty acids and dipeptides (Fig. S1A). The age-associated accretions in plasma acylcarnitines, which were inversely associated with estimated glomerular filtration rate (eGFR) [21], indicates declined renal function with aging. The kidney plays a key role in carnitine biosynthesis from lysine and methionine, and in carnitine excretion into the urine and plasma [22]. Pathway enrichment analysis of age-related metabolites uncovered several pathways related to urea metabolism, including “urea cycle and metabolism of amino groups”, “biomarkers for urea disorders” and “urea cycle and associated pathways” (Fig. S1B). The involvement of urea metabolism suggests perturbations in hepatic and/or renal function with aging [23]. A gross comparison between the age-associated plasma metabolite changes reported here with preceding metabolome perturbations in CKD revealed significant overlap, such as aberrant levels of short and medium-chain acylcarnitines [14,[24], [25], [26]], gut microbiota metabolites [26], tryptophan metabolites [27], purine metabolites [28,29], acetylated amino acids [[30], [31], [32]], bile acids [26], LPCs [26,33] and lysophosphatidylethanolamines (LPEs) [26,34] (Table S1).Table 1 Clinical characteristics of normative aging cohort.

Table 1Characteristic	Statistics, N = 225	
Sex (female), n (%)	114 (50.7 %)	
Age (years)	
 Mean (SD)	52.56(18.00)	
 Min-Max	20.00–88.00	
 Median(Q1,Q3)	52.00(38.00,68.00)	
BMI (kg/m2)	
 Mean (SD)	23.73(3.64)	
 Min-Max	14.61–36.41	
 Median(Q1,Q3)	23.89(21.35,25.77)	
 Unknown	57 (25.3 %)	
History of hypertension, yes (%)	39 (17.3 %)	
 Unknown	55 (24.4 %)	
History of coronary heart disease, yes (%)	13 (5.8 %)	
 Unknown	55 (24.4 %)	
History of diabetes, yes (%)	12 (5.3 %)	
 Unknown	55 (24.4 %)	
History of dyslipidemia, yes (%)	39 (17.3 %)	
 Unknown	55 (24.4 %)	
History of myocardial infarction, yes (%)	0 (0 %)	
 Unknown	55 (24.4 %)	
History of drinking alcohol, n (%)	27 (12 %)	
 Unknown	55 (24.4 %)	
SBP (mmHg)		
 Mean (SD)	123.03(16.70)	
 Min-Max	82.00–173.00	
 Median(Q1,Q3)	120.00(111.00,133.50)	
 Unknown	58 (25.8 %)	
DBP (mmHg)	
 Mean (SD)	72.87(11.61)	
 Min-Max	3.00–102.00	
 Median(Q1,Q3)	72.00(65.00,78.75)	
 Unknown	59 (26.2 %)	
TG (mmol/L)	
 Mean (SD)	1.25(0.83)	
 Min-Max	0.24–6.48	
 Median(Q1,Q3)	1.07(0.79,1.45)	
 Unknown	57 (25.3 %)	
TC (mmol/L)	
 Mean (SD)	4.73(0.84)	
 Min-Max	2.88–7.11	
 Median(Q1,Q3)	4.65(4.10,5.26)	
 Unknown	57 (25.3 %)	
HDL (mmol/L)	
 Mean (SD)	1.41(0.35)	
 Min-Max	0.78–2.70	
 Median(Q1,Q3)	1.38(1.17,1.61)	
 Unknown	57 (25.3 %)	
LDL (mmol/L)		
 Mean (SD)	2.81(0.73)	
 Min-Max	1.27–5.08	
 Median(Q1,Q3)	2.70(2.27,3.29)	
 Unknown	57 (25.3 %)	
CREA (umol/L)	
 Mean (SD)	64.85(14.72)	
 Min-Max	35.30–116.00	
 Median(Q1,Q3)	64.15(54.50,72.35)	
 Unknown	53 (23.6 %)	
BUN (mmol/L)	
 Mean (SD)	4.93(1.36)	
 Min-Max	2.40–9.50	
 Median(Q1,Q3)	4.70(3.90,5.83)	
 Unknown	53 (23.6 %)	
UA (umol/L)	
 Mean (SD)	323.23(76.17)	
 Min-Max	142.20–573.90	
 Median(Q1,Q3)	321.00(269.53 378.23)	
 Unknown	53 (23.6 %)	
RI (mU/L)	
 Mean (SD)	7.68(6.84)	
 Min-Max	1.90–81.50	
 Median(Q1,Q3)	6.80(5.20,8.60)	
 Unknown	80 (35.6 %)	
GLU (mmol/L)	
 Mean (SD)	5.32(0.73)	
 Min-Max	3.92–8.60	
 Median(Q1,Q3)	5.16(4.88,5.57)	
 Unknown	57 (25.3 %)	
BMI: body mass index, SBP: systolic blood pressure, DBP: diastolic blood pressure, Hcy: homocysteine, HSCRP: hypersensitive-c-reactive-protein, TG: triglycerides, TC: total cholesterol, HDL: high-density lipoprotein, LDL: low-density lipoprotein, CREA: creatinine, BUN: urea nitrogen, UA: uric acid, RI: regular insulin, GLU: glucose.

Standard linear regression analyses might overlook undulating metabolite alterations across aging. To overcome this, we next conducted unsupervised hierarchical clustering to group age-associated plasma metabolites that possessed similar trajectories of changes. Nine metabolite trajectories were obtained (Table S2, Figs. S1C–D), demonstrating the non-linear nature of several age-associated metabolite alterations. Classification of metabolite trajectory allows a clear visualization of temporal fluctuations in metabolite levels with age (Figs. S1D–E), and metabolites with common trajectory (i.e. within the same cluster) possibly indicate co-regulation across aging. We defined the metabolic nature of individual clusters based on the dominant metabolite class. Metabolites relevant to renal function were distributed amongst clusters 2–4, which predominantly comprised lipids, uremic toxins, N-acetyl-amino acids (N-acetyl-AAs) and acylcarnitines. Clusters 2 and 3 contained metabolites downstream of tryptophan and tyrosine metabolism, such as kynurenic acid, l-kynurenine and P-cresol-sulfate, which denote uremic toxins normally excreted by the kidneys. Uremic toxins accumulate under impaired kidney function and can inflict damages on multiple organs [29]. Cluster 3 also included several N-acetyl-AAs generated from catabolism of N-acetylated proteins. Under normal circumstances, N-acetyl-AAs are deacetylated and reabsorbed in the kidneys via amino acid salvage pathway, and their accumulation illustrates compromised renal salvage function [35]. A simple overview of age-related metabolic alterations thus underscores dysregulated renal function as a key aspect of systemic aging in humans.

2.2 DE-SWAN analysis identifies bursts in differential metabolites across aging

Since metabolic reactions are intertwined and often buffered by compensatory mechanisms, aging-associated metabolic phenotypes might be masked until resiliency mechanisms fall apart. Instead of a gradual, linear process, we therefore expect systemic aging to occur in waves from a metabolic perspective. Utilizing differential expression-sliding window analysis (DE-SWAN) [36] designed to select quantitative changes in phenotype throughout life, we uncovered three metabolic crests at ages 25, 56, and 72, which corresponded approximately to life stages of young, middle-aged, and old-aged in human (Fig. 1A). DE-SWAN algorithm analyzes metabolite changes in sliding windows (in increments of 1 year) of 20 years, and compares two groups in parcels of 10 years (e.g., 35–45 years compared to 45–55 years) throughout all ages examined [36]. DE-SWAN analysis unmasked the sequential effects of aging on the systemic metabolome and revealed several metabolite clusters that were altered in waves across aging (Fig. 1B). As anticipated, DE-SWAN analysis additionally identified 18, 20, and 25 metabolites specifically altered at ages 25, 56 and 72, respectively, which were not revealed by linear regression analysis (Fig. 1C). To obtain metabolic representation of these age-related crests, we conducted pathway enrichment analysis based on metabolites within individual clusters (Fig. 1D–Table S3). DE-SWAN analysis detected temporal dysregulation in metabolic pathways otherwise masked in linear modeling. For example, linear regression analysis indicated a general downregulation in “nicotinamide salvaging” and “nicotinamide metabolism” across aging, but DE-SWAN revealed that these pathways were upregulated at 25 years then downregulated at 56 years instead (Fig. 1D). Enhanced nicotinamide salving in young individuals and its subsequent decline at middle age are in agreement with the reported effect of increasing NAD + bioavailability on improving healthspan [37]. Importantly, pathways concerning the regulation of organic and/or inorganic ion and amino acid transport mediated by solute carrier proteins (SLC), such as “SLC-mediated transmembrane transport” and “transport of inorganic cations/anions and amino acids/oligopeptides”, were downregulated with aging, particularly at 72 years (Fig. 1D). Enrichment of these pathways was primarily ascribed to reductions in plasma isoleucine and leucine levels with aging. Indeed, appreciable reduction in plasma isoleucine was previously observed in patients with acute kidney injuries, attributed to abated activity of SLC6a19 neutral amino acid uniporter in proximal renal tubular cells - an early cellular response to kidney injuries resulting from ischemia [38]. As one of the most energy-demanding organs in the human body [39], the kidneys consume substantial amounts of ATPs in executing waste removal and nutrient reabsorption from blood, and in maintaining systemic electrolyte and fluid balance, which makes the kidneys particularly vulnerable to metabolic disturbances associated with aging. Indeed, amongst the top ten metabolites ranked by statistical significance in the old-aged cluster at 72 years, seven metabolites (i.e. tryptophan 2-C-mannoside, N-acetyl-l-alanine, pseudouridine, sn2 LysoPE(22:6), l-cystine, N-acetyl-l-aspartic acid, symmetric dimethylarginine) (Table S3) were reported to significantly correlate with eGFR [34]. To further highlight the decline in renal function within individual age-associated metabolic crests defined by DE-SWAN, we manually selected ten metabolites implicated in CKD pathogenesis based on published literature [26,34,40], which included symmetric dimethylarginine, l-kynurenine, citrulline, tryptophan 2-C-mannoside, succinyladenosine, pseudouridine, p-cresol glucuronide, p-cresol sulfate, creatinine and uric acid. The number of CKD-associated metabolites significantly altered in each of the three metabolic crests was compared, and the number of fluctuating CKD-associated metabolites was evidently highest at old-age (72 years), but statistical significance emerged as early as middle-age (56 years) (Fig. 1E and F).Fig. 1 (A) Schematic diagram illustrating the study design. Baseline blood samples were collected from CKD patients. Plasma and RBCs were isolated from the whole blood samples from 45 to 117 patients, respectively. The clinical follow-up period lasted for approximately 7 years ≈ 86 months, during which 11/45 patients in the plasma cohort and 37/117 patients in the RBC cohort recorded newly onset cardio-cerebrovascular events. RBCs from a subset of 40 patients (20 events) were also subjected to proteomics analysis. (B) Quantitative lipidomics was carried out to analyze the whole-lipidome of plasma (red rim) and polar lipidome of RBCs (blue rim). Lipid species were classified according to major lipid classes defined on the circumference, and the number of species detected in each lipid class was summarized on the upper left corner. The x-axis illustrates increasing double bond number (starting from left to right) while the y-axis denotes increasing carbon atom number (radiating from the interior to exterior region). The number of lipids for each specific combination of carbon atom numbers and double bond numbers was defined by the color of each dot. RBC: red blood cells; PC: phosphatidylcholines; PE: phosphatidylethanolamines; PG: phosphatidylglycerols; PI: phosphatidylinositols; PS: phosphatidylserines; SM: sphingomyelins; TG: triacylglycerols; CAR: acylcarnitines; CE: cholesteryl esters; Cer: ceramides; DG: diacylglycerols; FA: free fatty acids; Gb3: globotriaosylceramide; GlcCer: glucosylceramides; GM3: monosialo-dihexosyl gangliosides; LacCer: lactosylceramides; LPA: lyso-PA; LPC: lyso-PC; LPE: lyso-PE; LPI: lyso-PI; LPS: lyso-PS; PA: phosphatidic acids. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Fig. 1

2.3 Mapping human systemic metabolite patterns of aging to murine organs and tissues

The foregoing results indicate significant overlap between the metabolite profiles of human systemic aging and renal dysfunction. To examine if the aging-associated metabolite patterns in human is recapitulated in mice, we conducted in-depth metabolomics analyses of five major peripheral organs and/or tissues in mice, including the kidney, skeletal muscle, liver, heart, brown adipose and plasma from 40 mice at 6 months (n = 5), 12 months (n = 12), 16 months (n = 12) and 20 months (n = 11) of age (Fig. 2), respectively, which corresponded approximately to the age span of our human cohort (20–88 years old). Pearson correlation was used to calculate the correlation coefficients of individual metabolites from the respective murine organs and/or tissues with age. Unsupervised hierarchical clustering showed that age-associated metabolites from the human plasma and murine kidney were most similar, followed by murine liver and murine plasma (Fig. 2A). This approach, however, might overlook metabolite correlation coefficients between two groups that were opposite in directions. Scatterplots of age-associated metabolite correlations between human plasma and individual murine organs and/or tissues showed that relative to the human plasma, the murine kidney exhibited the greatest number of positively correlated age-associated metabolites (red circles), while the skeletal muscle displayed the highest number of negative correlations (blue circles) (Fig. 2B). A chord diagram was constructed for visualizing the directions of correlations across different tissues (Fig. 2C). For each tissue/organ, metabolites positively correlated with aging were categorized under the red section, while those negatively correlated were grouped under the blue section. Metabolites that were consistently positively correlated or negatively correlated with aging in both tissue/organs were depicted as red and blue bands, respectively. Metabolites displaying opposite directions of correlations between two tissues/organs in comparison were illustrated as gray bands. Corroborating preceding observations, consistently positive correlations (red bands) between human plasma and murine kidney, and opposite correlations (gray bands) between human plasma and murine skeletal muscles were observed (Fig. 2C). Closer inspection of metabolite identities revealed that these human metabolite mediators of aging recapitulated in mice were predominantly carboxylic acids and derivatives, which included fumaric acid, malic acid, creatine, creatinine, l-cystine, and tryptophan 2-C-mannoside for murine kidney; and succinic acid, N-lactoyl-phenylalanine, uric acid, l-leucine and citrulline for murine skeletal muscles (Fig. 2D). In addition, several age-associated metabolites recapitulated in mouse kidney are relevant to renal function (Table S4).Fig. 2 Cox proportional hazard model adjusted for age, sex and dialysis vintage. (A) Forest plot illustrates results from Cox regression of cardio-cerebrovascular events in RBC cohort (n = 117, 37 events). Multivariate-adjusted HRs per SD increment and 95 % CI of top 20 lipids that emerged significant (p < 0.05) were displayed. (B) Forest plot illustrates results from Cox regression of cardio-cerebrovascular events in plasma cohort (n = 45, 11 events). Multivariate-adjusted HRs per SD increment and 95 % CI of top 20 lipids that emerged significant (p < 0.05) were displayed. (C) Venn diagram compares the number of overlapping lipid predictors based on RBC and plasma cohorts. HR: hazard ratio; SD: standard deviation; CI: confidence interval.

Fig. 2

2.4 Murine kidney GluCer accumulation underlies age-associated renal functional decline

Our metabolomics-oriented investigation underscores enhanced susceptibility of the kidney to aging-induced functional decline. Aberrant lipid partitioning in CKD and ectopic renal lipid accumulation are known to undermine kidney function [41]. To elucidate molecular mechanisms underlying renal functional deficit with aging, we performed quantitative lipidomics on kidneys collected across four ages from both male (n = 19) and female (n = 23) mice (Fig. 3A). Changes in renal lipidomes across aging was sexually dimorphic (Fig. 3A). Female-specific accumulation of GluCer, acylcarnitines, cardiolipins (CLs), bis(monoacylglycero)phosphates (BMPs) and plasmalogen phosphatidylethanolamines (PE-Os) were observed with increasing age, while age-dependent accretions in neutral lipids including diacylglycerols (DAGs), triacylglycerols (TAGs) and cholesteryl esters (CEs) were detected in male kidneys (Fig. 3A). Weighted correlation network analysis (WGCNA) was applied for trans-omics integration of renal metabolome and lipidome data, which were transformed into distinct metabolite and lipid modules (Tables S5–6). The strength of coregulation between individual metabolite and lipid modules was measured by Pearson correlation coefficients and represented in a clustered heatmap (Fig. 3B). Metabolic signatures of individual metabolite modules were obtained by enriched pathways generated from over-representation analysis (ORA) of metabolites using the KEGG database, while lipid modules were defined by the predominant lipid classes within each cluster. We noticed prominent negative correlations between lipid module containing GluCer (L14) and metabolite modules denoting lysine degradation and arginine biosynthesis (Fig. 3B, insert). The kidney represents the major site of arginine biosynthesis primarily via cells of the proximal convoluted tubules (PCTs) [42]. The kidney is also the principal organ responsible for the metabolic turnover of lysine, and accelerated lysine degradation is renoprotective under hypertension [43]. Integrated omics showed that GluCer accumulation in the kidney was associated with renal functional decline. Total GluCer and individual GluCers with different fatty acyl chains began to accumulate in the kidneys of female mice at 16 months of age (Figs. S2A–B). Kidney GluCer levels were positively correlated with metabolites implicated in renal function and pathology, which include short- and medium-chain acylcarnitines and N1-methylated purines like 1-methyladenosine and 1-methylinosine (Fig. 3C–Table S7). Preceding genome-wide studies have illuminated the involvement of carnitine metabolism in CKD [44,45]. Amongst other organs, murine kidney and brain possess the highest levels of 1-methyladenosines [46], and elevated circulating 1-methyladenosine in CKD serves as early indicator of cellular damages induced by oxidative stress [47].Fig. 3 Kaplan-Meier survival curves and logrank test comparing cardio-cerebrovascular outcome in patients categorized by concentration tertiles of lipids in (A) RBCs and (B) plasma. For RBCs, top lipid with smallest logrank test p value in each lipid class was shown. For plasma, top six lipids with logrank test p < 0.05 were shown. (C) Receiver operating curves (ROCs) of adverse cardio-cerebrovascular outcomes in dialysis patients based on logistic regression models. A total of 80 versus 37 events were recorded in the RBC cohort, while 34 versus 11 events were recorded for the plasma cohort over a follow-up duration of 96 months. The base model (age + sex + dialysis vintage) was compared with a model that incorporated top three significant lipids from Cox regressions in the RBC cohort and plasma cohort, respectively. DeLong's test was used to compare the performance of individual models.

Fig. 3

2.5 Age-related increases in circulating GluCer are conserved in human and predictive of death

Euclidean clustering using correlation coefficients between lipids and age revealed that the patterns of age-associated sphingolipid changes in murine plasma were most similar to that in the liver and kidney (Fig. 3D). Confining our analyses to only GluCer species commonly detected in the plasma and individual organs/tissues, the shortest Euclidean distance was again measured between plasma and the kidney (Table S8). These results highlight kidney as the major peripheral organ contributing to age-dependent changes in circulating GluCer. DE-SWAN analysis of quantitated GluCer levels in plasma samples from the cross-sectional aging cohort (Table 1) uncovered a small wave of GluCer increases at 54–55 years (Fig. 3E). Circulating GluCers exhibited increasing trends with age (Fig. S2C, Table S9), which were particularly significant in females (Fig. S2D). DE-SWAN analysis revealed a crest in circulating GluCer levels of females at 54–61 years following a trough from 47 to 51 years, while a small crest at 71–72 years was observed in males (Fig. S3A). Total plasma GluCer was significantly and positively correlated with age for females, but not for males (Figs. S3B–C). Of interest, DE-SWAN analysis uncovered waves of increases in circulating uremic toxins at 51 and 71 years for females, and at 71 years for males (Fig. S3D), which closely mirrored temporal waves of increases in circulating GluCer. A closer look at the age-dependent variations in major uremic toxins including uric acid, creatinine and l-Kynurenine showed that females generally maintained considerably lower levels of these uremic toxins relative to males up to about 50 years old, following which the levels of uremic toxins increased steadily to comparable levels in males after 60 years old (Fig. S3E). Thus, the rise in plasma uremic toxins closely coincided with temporal crests of GluCer accretion throughout the life course of females. In a separate longitudinal cohort of elderly people (n = 271) (Table 2), baseline levels of circulating GluCer d18:1/20:0 and GluCer d18:1/24:0, together with PC38:4, were identified as top three variables predictive of deaths within a follow-up period of six years (Fig. 3F). The combinatorial panel of circulating lipids and clinical indices measured at baseline were predictive of deaths predominantly resulting from organ failure, infection or cardiovascular events with an area under curve (AUC) of 0.861 (95 % CI = 0.814–0.908) using lasso regression analysis (Figs. S3F–G). Therefore, age-associated increases in circulating GluCer, largely contributed by the kidneys, are conserved from mice to human, and are significantly associated with enhanced risk of multiple causes of deaths in aged individuals.Table 2 Clinical characteristics of longitudinal cohort of elderly people.

Table 2Characteristic	Deaths, N = 94	Survivors, N = 177	p-value	
Age(years)			<0.001a	
 Mean (SD)	76.44(4.29)	74.54(3.75)		
 Min-Max	69.00–85.00	69.00–85.00		
 Median(Q1,Q3)	76.00(73.00,80.00)	74.00(71.00,77.00)		
Sex, n (%)			0.109b	
 Male	61 (65 %)	97 (55 %)		
 Female	33 (35 %)	80 (45 %)		
Smoking, n (%)			0.076b	
 Non-smoker	42 (56 %)	102 (66 %)		
 Smoker	10 (13 %)	8 (5.2 %)		
 Ex-smoker	23 (31 %)	44 (29 %)		
 Unknown	19	23		
BMI, kg/mb			0.443a	
 Mean (SD)	25.23(3.48)	24.89(3.05)		
 Min-Max	16.08–36.39	17.89–32.18		
 Median(Q1,Q3)	24.85(22.89,27.61)	24.92(22.91,27.03)		
 Unknown	5	6		
eGFR, mL/min/1.73 mb			<0.001a	
 Mean (SD)	62.43(18.25)	72.04(19.25)		
 Min-Max	28.69–112.98	26.05–131.18		
 Median(Q1,Q3)	59.80(50.19,73.36)	71.37(58.33,84.75)		
eGFR: estimated glomerular filtration rate.

a Welch Two Sample t-test.

b Pearson's Chi-squared test.

2.6 Trans-omics elucidation on the function of circulating GluCer across aging

Using published datasets of human and mouse plasma proteomes across aging [36], we conducted trans-omics integration of senescence-associated plasma proteome with metabolome and lipidome obtained in our study. Distinct human and murine protein-lipid clusters were captured (Tables S10–S11), which comprised proteins and lipids/metabolites exhibiting comparable temporal patterns across aging. ORA analysis of enriched proteins within individual clusters were performed against the Disease Ontology (DO), Gene Ontology (GO) and KEGG pathway database to define the biological function of these senescence dynamic clusters (Fig. 4A). Individual GluCer species were distributed amongst H_cluster 2, H_cluster 4 and H_cluster 5 of the human protein-lipid clusters, and M_cluster 2 and M_cluster 7 of the murine protein-lipid clusters, respectively. H_cluster 2 and M_cluster 7 possessed very similar temporal patterns across aging, with sharp increases particularly from young through the middle age, and attenuated increases in old age. Both clusters comprised GluCer d18:0/22:0, GluCer d18:0/24:1, GluCer d18:0/24:0 and GluCer d18:1/22:0 that were predominantly dihydro-GluCer species containing very long-chain fatty acyls (≥C22) (Fig. 4B). The matching temporal patterns of changes in these GluCer across senescence suggest that these lipids might have conserved functions between human and mice that are relevant to systemic aging. Top 15 DO terms ranked by statistical significance conserved across these five GluCer-associated clusters implicated several peripheral organs and tissues, including the kidney (nephritis, glomerulonephritis), heart (myocardial infarction) and lungs (obstructive lung disease, chronic obstructive pulmonary disease) (Fig. 4C), which suggests that circulating GluCer accumulation might impact on multiple organs across aging. With regard to GO terms, “neutrophil migration” and “granulocyte migration and chemotaxis” were most significantly enriched terms across the five GluCer-associated clusters (Fig. 4D). The “cytokine-cytokine receptor interaction”, “JAK-STAT signaling pathway” and “AGE-RAGE signaling pathway in diabetic complications” emerged as the most significant pathways associated with these GluCer clusters from ORA analysis using the KEGG pathway database (Fig. 4E, Fig. S4).Fig. 4 Correlations between clinical/biochemical indices with RBC and plasma lipids. Weighted correlation network analysis was performed with the R package “WGCNA” that categorized the (A) RBC lipidome and (B) plasma lipidome into modules eigengenes (ME), and measured the strength and direction of correlation between individual modules and clinical traits. Each ME is defined by the first component from a principal component analysis representative of the overall module expression. Enrichment analyses of lipid class was performed for individual modules using hypergeometric tests. Correlations between lipid levels and clinical indices were examined using Pearson correlation analysis, with red circles indicating positive correlations and blue circles indicating negative correlations, and size of the circles corresponding to magnitude of correlation coefficients. Polyunsaturated PCs (PUFA-PCs) consistently represented in RBC lipidome ME1 and plasma lipidome ME6 included PC 36:5, PC 38:6, PC 39:6, PC 40:7, PC 40:6, PC 42:7, PC 42:6, PC 42:5, PC 42:3. Very long-chain (VLC)-Cer and VLC-SM represented in plasma lipidome ME9 included species with acyl chains of carbon atom numbers C22–C26. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Fig. 4

2.7 Mitochondria activity of PCTs from older mice are sensitive to GluCer accumulation

Biosynthesis of GluCer involves the addition of glucose moiety to Cer precursors mediated by UDP-glucose ceramide glucosyltransferase (UGCG) in the Golgi apparatus [48], while glucocerebrosidase/glucosylceramidase (GCase) is a lysosomal enzyme that breaks down GluCer into glucose and Cer constituents. GCase is encoded by the glucosylceramidase beta (GBA) gene located on chromosome 1q21. To further explore the biological effect of GluCer accumulation on renal metabolism and uremic toxin production, PCTs were isolated from kidneys dissected from young (1-month old) and old (20-month old) mice and treated with 20 μM or 100 μM of conduritol-β-epoxide (CBE) (Fig. 5A). CBE covalently and irreversibly binds to the catalytic site of GCase, thereby leading to irreversible inactivation of the enzyme and subsequent GluCer accumulation [49]. PCTs were selected for assay because these cells are primarily responsible for the elimination of various exogenous as well as endogenous toxins (i.e. uremic toxins) by the kidneys. Many transporters from the SLC superfamily, particularly the organic anion transporters (OATs) handling the active removal of uremic toxins coupled to the activity of Na+K+-ATPase, are localized on the membranes of PCT cells (PCTCs) [50]. Oxygen consumption rates (OCR) of PCTCs isolated from young and old mice treated with control saline or CBE (20 μM or 100 μM) were measured by the Seahorse XeF96 extracellular flux analyzer and the basal respiration, maximal respiration and ATP production from these cells were calculated (Fig. 5B and C). The oxygen consumption rates of PCTCs isolated from young mice were unaffected by CBE both at 20 μM or 100 μM, indicating that these concentrations of CBE were not physiologically toxic to the mitochondria of PCTCs. The OCR, respiratory rates and ATP production of PCTCs from old mice, however, were significantly decreased with CBE treatment relative to control group. In addition, CBE treatment at both concentrations led to significantly elevated GluCer levels in PCTCs from both young and old mice (Fig. 5D). Also, in accordant with our preceding observations, GluCer levels were higher in untreated PCTCs from old compared to young control mice (Fig. 5D). PCTCs in elderly mice had altered lysosomal and mitochondrial morphology, including enlarged lysosomes and decreasing mitochondria intensity (Figs. S5A–C). It has been reported that there was disrupted mitochondrial distribution and function in Parkinson's patient derived GBA1-linked neurons. Mitochondrial dysfunction due to prolonged mitochondria-lysosome contacts was partially rescued by TBC1D15 expression [51]. Immunoblot analyses revealed a higher TBC1D15 expression in PCTCs of 3 month-old mice compared to 26 month-old mice (Figs. S5D–E). The foregoing data indicate that while mitochondria of young PCTCs can accommodate elevated levels of GluCer resulting from impeded hydrolysis, mitochondria in PCTCs of old mice are sensitive to GluCer accretion, as shown by compromised oxidative phosphorylation and ATP production. Metabolomics revealed that PCTCs of old mice treated with 100 μM CBE were significantly elevated in numerous metabolite classes relative to untreated controls, particularly for carboxylic acids and derivatives and purine nucleotides (Fig. 5E). Several of these metabolites are known metabolite markers of CKD, such as pseudouridine, symmetric dimethylarginine, 1-methylguanosine, l-kynurenine, 1-methyladenosine and other uremic toxins (Table S12). Reactome pathways involving nucleotide metabolism and nucleotide salvage denote the most significantly altered metabolic pathways following CBE treatment at 100 μM relative to control saline. (Fig. 5F).Fig. 5 Proteomics analysis of erythrocytes. (A) RBC proteome of patients with and without new-onset cardio-cerebrovascular events were moderately separated based on principal component analysis. (B) Volcano plot illustrates erythrocyte proteins that were significantly different between non-CCV and CCV groups (Student's t-test p < 0.05) (C) Over-representation analysis of significant pathways based on differential proteins using the KEGG database, only pathways with p < 0.05 were displayed. (D) Boxplots comparing the levels of erythrocyte proteins in non-CCV versus CCV. Non-CCV: patients without new-onset cardio-cerebrovascular events; CCV: patients with new-onset cardio-cerebrovascular events; p-values were from Student's t-test.

Fig. 5

To demonstrate that eradicating GluCer accumulation can restore mitochondrial function in PCTCs, we utilized Eliglustat (EG), a specific and potent inhibitor of GluCer synthase that is currently used for the treatment of Gaucher's disease. As it was shown that only PCTC mitochondria from old mice are sensitive to GluCer increase (Fig. 5B and C), we utilized PCTCs isolated from old mice for subsequent cell-based experiments. PCTCs obtained from 20-month old mice that were subjected to a combinatorial addition of both CBE (50 μM) and EG (500 nM) had the OCR level restored to a similar level as that of controls (Fig. 5G). Conforming with expectations, EG enhanced maximal respiration of PCTCs relative to control, and addition of EG to CBE-treated PCTCs significantly improved basal respiration, maximal respiration and enhanced ATP production compared to CBE-treated cells (Fig. 5H). EG treatment also reversed the metabolome profiles of PCTCs relative to CBE treatment. For example, pseudouridine, creatine, tryptophan 2-C-mannoside and other uremic toxins, which were increased with CBE, were reduced in PCTCs treated with EG (Table S13). We further extended the experiments by treating human kidney proximal tubular epithelial cells (HKCs) with 10 μM CBE for 48 h (Fig. S6). Consistent with results from mouse cultured cells, CBE treatment significantly increased the content of acylcarnitines and uremic toxins in HKCs (Fig. S6A). Consistent with increases in acylcarnitines that were indicative of perturbed mitochondrial β-oxidation, metabolic flux analyses revealed compromised OCR in CBE-treated cells, which was ameliorated with the addition of EG (Fig. S6B). In HKCs, the addition of EG also restored maximal respiration and ATP production compared to cells treated with CBE alone (Fig. S6C). Immunoblot analyses revealed enhanced mitochondrial fission and abated mitochondrial fusion in HKCs treated with 10 μM CBE, reflected by elevated dynamin-related protein 1 (Drp1) and reduced optic atrophy 1 (Opa1) levels, respectively (Fig. S6D). Perturbed mitochondria dynamics lowers ATP production and exacerbates cellular senescence [52], in line with the elevated expression of p53 protein observed in CBE-treated cells (Figs. S6D–E).

2.8 Resiliency mechanisms of late middle-aged females in coping with renal GluCer accumulation

As sharp increases in circulating uremic toxins were observed in human females at the late middle-age stage (approximately 50–60 years) (Fig. S3E), we created a murine model to investigate resiliency mechanisms that maintain renal function in spite of GluCer accumulation in late middle-aged mice. A murine model of GluCer accumulation was established by intraperitoneal injection of young (4-month old) and late middle-aged (14-month old) female mice with CBE (100 mg/kg/day) for a period of 21 days, and control group underwent saline injection (Fig. 6A). Late middle-aged mice exhibited close to 40 % reduction in body weight at 21 days with CBE injection, while no significant changes in body weight were noted for young mice (Fig. 6B). CBE injection resulted in significant increases in kidney GluCer levels in both young and late middle-aged mice (Fig. 6C), but pathological changes in renal tubules were observed only in late middle-aged mice (Fig. 6D and E). Haematoxylin-eosin staining showed that the renal tubule morphology of CBE-injected mice was more irregular with ectopic lipid accumulation represented by white unstained areas (Fig. 6D). The ratio of unstained areas to total surface areas of renal tubules was calculated to reflect the degree of renal tubule damage, which was elevated in CBE-treated mice relative to control mice (Fig. 6E). Volcano plot illustrates that long-chain acylcarnitines, creatine and creatinine were elevated, while nucleotides such as uridine, guanosine and hypoxanthine were reduced (Fig. 6F–Table S14). Pathway analysis using the Reactome database showed that several pathways implicating SLC-mediated transmembrane transport of cations/anions, amino acids and organic acids, the citric acid cycle and respiratory electron transport, as well as purine catabolism, were upregulated in CBE-treated late middle-aged mice (Fig. 6G). These pathways might constitute the resiliency mechanisms that help female mice cope with GluCer accumulation at late middle-age phase preceding the surge in uremic toxins at a later stage. A detailed look into metabolite changes under the purine catabolism pathway showed that, contrary to observations in PCTCs isolated from old mice, purine nucleosides and bases were reduced in CBE-treated middle-aged mice, while xanthine and uric acid that are downstream end-products of the catabolic pathway, were upregulated (Fig. 6H and I).Fig. 6 Lipid-driven modifications in erythrocyte properties and metabolism that alter the risk of CCV outcome. Combinatorial lipidomics and proteomics revealed that enhanced levels of anionic phospholipids (PA, PS, PI) in the erythrocytes of patients predisposed to CCV outcome were associated with (1) altered levels of tropomyosin isomers leading to perturbed RBC morphology; (2) enhanced glucose uptake to fuel glycolysis and the pentose phosphate pathway to produce reducing equivalents for driving the erythrocyte battery of antioxidant systems such as the GSH cycle; and (3) elevated Band 3-mediated gaseous exchange. Erythrocytes of predisposed patients also exhibited augmented focal adhesion resulting from reduced levels of major syaloglycoproteins such as glycophorin A and glycophorin C that normally serve to minimize cell-cell interactions and RBC aggregation, and augmented RBC-endothelium adhesive interactions increases the risk of microvasculature occlusion, leading to adverse CCV outcome. (B) Ratio of outer plasma membrane phospholipids (PC, SM) to inner membrane phospholipids (PA, PS, PG and PI) was significantly lower in dialysis patients with new-onset CCV events. GSH: glutathione; GSSG: oxidized glutathione; PPP: pentose phosphate pathway; GSR: glutathione reductase; PC: phosphatidylcholines; SM: sphingomyelins; PA: phosphatidic acids; PS: phosphatidylserines; PI: phosphatidylinositols.

Fig. 6

2.9 Cellular purine depletion alleviates mitochondrial dysfunction triggered by GluCer accumulation

We then explored molecular mechanisms that underpin mitochondrial dysfunction with CBE-induced GluCer accumulation. Kidney tissues of young and late middle-aged female mice after 21 days of CBE treatment were collected for immunoblot analyses (Fig. 6). CBE treatment effectively reduced the levels of TBC1D15 protein in renal tissues of middle-aged and young mice. Furthermore, the protein level of TBC1D15 in middle-aged mice was lower than that in young mice, and after CBE treatment, the TBC1D15 level in middle-aged mice also showed a decreasing trend compared to young mice (Figs. S5F–G). Renal GluCer accumulation perturbs mitochondria dynamics by impairing fusion-fission balance in late middle-aged females, as evident in increased level of the mitochondrial fission protein Drp1 and decreased level of the fusion protein Opa1 (Fig. 6A–C), consistent with the results of CBE-treated HKCs (Fig. S6D). Tissue homogenate was immunoblotted for protein levels of PTEN-induced kinase 1 (Pink1), p53, Ser2448 phosphorylation of the mammalian target of rapamycin complex 1 (mTORC1) and total mTORC1, the mTORC1-dependent Thr389 phosphorylation of the ribosomal S6 kinase (S6K) and total S6K (Fig. 6A–H). mTORC1 activation in late middle-aged female mice treated with CBE was reflected by increased phosphorylated forms (i.e. p-mTOR/mTOR and p-S6K/S6K) of both mTORC1 and S6K (Fig. 6G and H), indicating mTORC1 activation and perturbed mitophagy [53]. The unaltered level of Pink1 (Fig. 6D), which selectively accumulates on depolarized mitochondria [54], showed that a majority of the renal mitochondria remained polarized and functional. Protein level of the senescence marker p53 was also unchanged in both young and middle-aged female mice treated with CBE (Fig. 6E). As intracellular purines were known to modulate mTORC1 activation [55], we used the Seahorse analyzer to examine the metabolic flux of PCTCs in response to CBE-triggered GluCer accumulation under various manipulations of endogenous purine metabolism. PCTCs isolated from these mice were treated with CBE for 24 h, with or without inhibition of de novo purine biosynthesis, using the inhibitors methotrexate (MTX) and 6-mercaptopurine (6-MP) that specifically target dihydrofolate reductase (DHFR) and hypoxanthine-guanine phosphoribosyltransferase (HPRT1), respectively. Interestingly, PCTCs treated with both CBE (20 μM) and 6-MP (20uM) or MTX (2uM) had OCR levels restored to similar level as that of saline-treated control group (Fig. 6I and J). Conforming to expectations, CBE treatment reduced the basal respiration and ATP production of PCTCs relative to control group, which was restored via concurrent additions of either 6-MP or MTX (Fig. 6I and J). The foregoing results demonstrate that the adverse effect of GluCer accumulation on PCTC mitochondrial metabolism was mediated by mTORC1 activation, and was ameliorated by depletion of endogenous purines. These findings align with our observations that while CBE-triggered GluCer accumulation increases endogenous purines in old mice, it reduces both adenylate and guanylate nucleotides in the PCTCs of late middle-aged mice (16-month old) that exhibited a milder phenotype of renal dysfunction - with majority of the mitochondria pool remaining functional (unaltered Pink1) (Fig. 6D) and the energetically costly pathways of transmembrane ion transport functionally intact (Fig. 6G).

3 Discussion

Despite being a central hub of mammalian metabolism, the metabolic importance of the kidney in modulating systemic aging is relatively understudied. The kidney devotes high energy expenditure toward maintaining electrolyte and fluid homeostasis, and in ensuring efficient waste removal and nutrient uptake, making it particularly susceptible to age-induced metabolic disturbances [39]. Our metabolism-focused approach unraveled enhanced vulnerability of the kidney to age-related functional decline, as evident by the predominance of metabolites associated with renal function in the old-aged cluster at 72 years from DE-SWAN analysis. Cross-tissue/organ correlation analyses unveiled that a majority of the aging characteristics observed in the human plasma was attributed to age-associated changes in the kidney. Deteriorated renal function induces an augmented release of uremic toxins into the circulation, which serve as systemic metabolite mediators triggering aberrant inter-organ crosstalk and multiple organ dysfunction [56], thereby accelerating systemic aging. Previous studies revealed that the decline in kidney function increases the risk of developing cardiovascular diseases by two-to fourfold [57], with advanced stage (stage 3–5) CKD patients having significantly elevated risk [58].

Through integrating metabolomics and lipidomics data, we discovered sexually dimorphic alterations in the kidney lipidome across aging. In particular, renal accumulation of GluCer was evident in females from the late middle-age phase onwards, this phenomenon was conserved across human and mice. Importantly, late middle-age onset accumulation of GluCer closely mirrors the surge in circulating uremic toxins in human females between 50 and 60 years old. These observations point to a plausible link between renal GluCer accumulation and a sharp decline in kidney function, reflected by the elevated release of uremic toxins into the circulation, specifically in females. In this regard, tissue GluCer has been implicated as a determinant of organ size, and enzymes controlling GluCer levels (i.e. UGCG and GCase) were demonstrated to be under hormonal control. Testosterone was shown to increase UGCG activity while reducing GCase activity, giving rise to enhanced GluCer levels and rapid kidney growth. On the other hand, estradiol was found to attenuate kidney growth by increasing GCase activity and suppressing UGCG activity in mice [59]. We postulate that the female-specific accretion in kidney GluCer observed at the late middle-age may be ascribed to decline in estradiol level with aging. Indeed, the rise in GluCer levels coincided with the age range of menopause onset for human females (ca. 45–55 years), and also corresponded to a period of drastic estradiol reductions measured in female mice between 12 and 17 months of age [60].

We then explored the systemic effects of senescence-associated GluCer accumulation by combinatorial analyses of plasma lipidomics and proteomics data across aging in human and mice. We emphasized on lipid-protein modules with conserved functions between the two species, which highlighted the role of circulating GluCer in modulating the migration and chemotaxis of immune cells. Increased production of proinflammatory cytokines, including various interleukins and chemokines, had been previously reported for human and murine macrophages challenged with GluCer overload, and immune cells of Gaucher's disease patients are “primed” for facilitated release of cytokines [61,62]. In a similar light, elevated circulating GluCer during senescence may promote cytokine release following immune cell uptake, thereby skewing systemic immune profiles toward the proinflammatory end of the spectrum. Age-associated increases in plasma GluCer were also positively associated with activation of the AGE-RAGE signaling pathway. Advanced glycation end products (AGEs) denote a diverse array of compounds produced by non-enzymatic interactions between reducing sugars and associated biomolecules that include proteins, lipids or amino acids [63]. AGEs bind to the receptor for AGE (RAGE), which sequentially produces intracellular reactive oxygen species (ROS) and further stimulates several intracellular signaling molecules that cumulate in the production of cytokines and other proinflammatory factors [64,65]. Some AGEs like beta-2-microglobulin are also classified as uremic toxins [66]. Blocking GluCer biosynthesis was found to ameliorate AGE-induced inhibition on mesangial cell proliferation that underpins the pathogenesis of glomerulosclerosis [67].

We next created in vitro and in vivo models of GluCer accumulation using the GCase inhibitor CBE to investigate its effects on kidney metabolism. GluCer overload was previously shown to distort mitochondrial cristae morphology and interfere with mitochondrial respiration in dorsal root ganglion neurons [68]. GBA mutations also impair mitophagy via disrupting mitochondrial priming and autophagy induction [69]. Mitochondrial dysfunction due to prolonged mitochondria-lysosome contacts is partially rescued by TBC1D15 expression in PD patient-derived mutant GBA1 dopaminergic neurons [51]. Consistent with previous reports, we observed altered mitochondrial morphology, afflicted mitochondrial respiration, and decreasing TBC1D15 expression in PCTs isolated from old mice. Defective mitochondrial function was reversed by abrogating GluCer accumulation via addition of EG. Mitochondria from PCTs of young mice were resilient to GluCer overload. Of interest, we observed that the metabolome profiles of PCTs were reversed upon restoration of mitochondrial function by modulating GluCer bioavailability. In particular, the level of pseudouridine emerged as negatively associated with mitochondrial function. Pseudouridylation of the mitochondrial 16S rRNA is essential to the stability and assembly of the mitochondrial ribosome; and disrupting such epitranscriptomic modification can interfere with the proper assembly of the oxidative phosphorylation complexes [70].

To elucidate resiliency mechanisms that help females cope with GluCer overload prior to overt renal functional decline at old age, we challenged late middle-aged female mice with CBE. ORA analysis of enriched pathways suggest that the kidneys handle GluCer overload by increasing SLC-mediated transmembrane transport, which translates to higher energy demand likely offset by enhanced activity of the TCA cycle and respiratory electron transport chain. GluCer overload in late middle-aged mice compromises mitochondrial function. Basal respiration and ATP production were diminished, which were restored by depleting intracellular purines. An unaltered level of Pink1, however, showed that the overall mitochondrial pool likely remains functionally intact with normal membrane potential despite the activation of mTORC1. Inhibition of mTORC1 signaling regulates Pink1/Parkin-mediated targeting of depolarized mitochondria to the autophagic machinery, and mTORC1 hyperactivation impedes mitophagy [53]. The metabolic scenario in late middle-aged female mice likely reflects early compensatory responses to renal GluCer overload. In response to ectopic GluCer accumulation, late middle-aged female mice exhibited accretion of xanthine and uric acid, which constitute the final end-products of purine catabolism, while adenylates and guanylates were reduced. Both adenylates and guanylates promote mTORC1 activation [71,72], while uric acid might have a dual effect [73,74]. Uric acid overload reinforces mTORC1 inhibition via activating AMP-activated protein kinase (AMPK) in PCTCs [74], but activates mTORC1 in monocytes by promoting the phosphorylation of proline-rich AKT substrate 40 (PRAS40) [73]. The cumulative results indicate that augmented purine catabolism in PCTs at late middle-age may serve to maintain adenylates and guanylates at low levels to attenuate mTORC1 activation in order to maintain mitochondrial function. Nevertheless, chronic activation of mTORC1 stimulates purine biosynthesis via increasing the expression of methylenetetrahydrofolate dehydrogenase 2 [75], leading to a positive feedback loop of purine biosynthesis and mTORC1 activation with renal aging that eventually jeopardizes mitophagy. Accumulation of damaged mitochondria imperils oxidative phosphorylation that fuels normal kidney function.

Our findings showed that the adverse effect of GluCer accumulation on renal mitochondrial metabolism during female aging is mediated via mTOR and modulated by intracellular purine levels. mTOR is able to sense changes in GluCer levels that subsequently trigger its activation [76], and GluCer-mTOR signaling induces redistribution of peroxisomes within mammalian cells [77]. Inhibition of mTORC1 enhances longevity [78]. mTORC1 signaling also regulates mitochondrial oxidative function [78,79]. mTORC1 inhibition is required for mitophagy to ensue, and hyperactivation of mTORC1 resulting from tuberous sclerosis complex (TSC) ablation increases the occurrence of dysfunctional mitochondria loaded with oxidized mitochondrial proteins [80]. Depletion of purine nucleotides suppresses mTORC1 signaling, which can be re-stimulated upon the addition of exogenous purines [71]. Our discovery of purine-modulated, mTORC1-mediated mitophagy as a female-specific pathway of renal senescence triggered by GluCer accretion is in agreement with the known roles of mTORC1 hyperactivity in numerous late-onset or age-related pathologies. For example, enhanced mTORC1 activity was shown to promote aging of the liver via triggering age-associated defect in ketogenesis [81]. Corroborating our findings, sexual dimorphism in the effect of mTOR signaling on lifespan extension was previously reported. Female mice treated with rapamycin displayed an 18 % increase in lifespan, whereas only a 10 % increase was observed in males [82]. Additionally, knockout of the mTORC1-dependent S6K1 extends lifespan specifically in female mice, with no longevity benefit for males [83]. Female-specific renal accumulation of GluCer commencing from late middle-age therefore confers a molecular explanation on the female-biased benefits on lifespan extension with mTOR inhibition.

Furthermore, we discovered purine catabolism as a resiliency mechanism that antagonizes GluCer-mTOR signaling in female kidneys at late middle-age. Investigating compensatory mechanisms that attenuate aging at late middle-age is meaningful in terms of uncovering new molecular targets for intervention. In our study, the greatest number of age-associated metabolites occurred in the 56-year-old window, in agreement with preceding work on aging brain that identified the window of 50–55 years as a predominant period of metabolic transition [84]. The antagonistic pleiotropy theory of aging argues that traits conferring early reproductive advantage might elicit deleterious effect later in life [85]. The female kidneys evoke a higher metabolic cost in maintaining homeostasis of electrolytes and ions than male kidneys, ascribed to a downward shift in sodium chloride reabsorption at later segments past the proximal tubules with a heavier reliance on Na+K+-ATPase [86]. Sex-dependent differences in the expressions of organic solute transporters (OATs) also contribute to the enhanced malleability of female kidneys to cope with varying workload, enabling the female kidneys to adapt to changing nutritional requirements of serial pregnancies (e.g. in diverting fluids and electrolytes to the developing fetus or the mammary glands) in order to optimize reproduction [87]. OAT2, for example, exhibits female-biased expression that is stimulated by estradiol and progesterone, but inhibited by testosterone [88]. The plasticity in renal function in response to changing estrogen levels during the female reproductive phase might render females liable to abrupt decline in kidney function induced by GluCer-mTOR signaling post-reproduction when estrogen levels fall. The higher energy expenditure incurred in maintaining kidney function for females also increases female susceptibility to renal mitochondrial dysfunction. Failure to sustain such high energy demand jeopardizes renal homeostasis and initiates a downward metabolic spiral that accelerates age-associated functional decline. Our current observations thus corroborate epidemiological findings reporting a higher prevalence of CKD in women compared to men, particularly post-menopause [89]. The lifetime risk of kidney failure, however, is higher in men than in women, which might be attributed to renoprotective resiliency mechanisms fueled by female hormones earlier in life [90].

4 Conclusion

Our trans-omics, metabolism-focused approach to normative aging uncovers the kidney as the Achilles heel of systemic aging in human and mice (Fig. 6). Age-associated GluCer accumulation perturbs renal homeostasis and unleashes a surge of uremic toxins into the systemic circulation, which deleteriously affects inter-organ crosstalk and speeds up senescence in multiple organ systems. Multi-omics data integration also revealed that circulating GluCers are functionally associated with the activation of proinflammatory pathways in both human and mice. Our findings underscore GluCer overload in female kidneys that commences at late middle-age, which in concert with senescence-associated changes in purine metabolism, jeopardizes mTORC1-mediated regulation of mitophagy. The resultant mitochondrial dysfunction signals the disintegration of renal homeostasis and generates temporal wave of circulating uremic toxins that propels systemic aging. The deleterious effects of GluCer-mTOR signaling in the orchestration of renal mitochondrial dysfunction comes into play at late middle-age, possibly ascribed to the drastic fall in estrogen levels that subsequently uplifts the inhibition on UGCG activity [59]. In all, our work unravels a female-biased susceptibility to systemic aging triggered by renal accumulation of GluCer, which may denote an evolutionary trade-off between somatic maintenance and successful reproduction earlier in life.

5 Methods

5.1 Study design and participants

Cross-sectional cohort. Fasting blood samples (n = 225) used in this study were collected in the Beijing Anzhen hospital, Capital Medical University, with written informed consent obtained from all participants. This study was approved by the Ethics Committee of Beijing Anzhen Hospital Capital Medical University, approval number 2018010. Demographics of the participants were presented in Table 1.

Longitudinal cohort. Study participants (n = 271) were a subcohort of a randomized placebo-controlled trial of vitamin B12 supplementation in older people with diabetes mellitus and mild vitamin B12 deficiency, recruited between 2011 and 2013 in the Prince of Wales Hospital of Hong Kong. Fasting blood samples were collected at baseline. The intervention showed no significant changes in cognitive function over 27 months [91]. All subjects were followed up for up to six years. Death was ascertained from the death registry of Hong Kong. Both the clinical trial and the extended follow-up study were approved by the medical ethics committee of Chinese University of Hong Kong and New Territories East Cluster of Hospital Authority of Hong Kong, and the trial was registered at the Clinical trial registry of the US (NCT02457507). Demographics of the study participants were presented in Table 2.

Fasting blood samples were collected in EDTA tubes. Blood samples were centrifuged at 3000 rpm for 10 min at 4 °C, and plasma was obtained according to standard procedures. Aliquots of plasma samples were kept frozen at −70 °C until further analysis.

5.2 Polar metabolite extraction

Polar metabolites were extracted from plasma as previously described [18,20]. Brieﬂy, 50 μL of plasma were mixed with 200 μL of ice-cold methanol containing 0.37 mM phenylhydrazine, vortexed for 10 s, and incubated for 30 min at 1500 rpm and 4 °C in an orbital shaker, then centrifuged for 10 min at 12 000 rpm and 4 °C. The supernatant was transferred into a clean 1.5 mL centrifuge tube, and dried using a SpeedVac (Genevac miVac, Tegent Scientifc Ltd., England). The dried extracts were redissolved with 2 % acetonitrile in water, centrifuged for 2 min at 9000 rpm, and clean supernatant was collected for LC-MS analysis.

5.3 Lipid extraction

Lipids were extracted using a modified version of the Bligh and Dyer's protocol [20]. 750 μL of ice-cold chloroform:methanol (1:2, v/v) was added to 100 μL plasma in a fresh 1.5 ml Eppendorf brand safe-lock tube placed on ice. The samples were vortexed for 15 s and incubated for 30 min in 4 °C cold room at 1500 rpm. At the end of incubation, 350 μL of ice-cold deionized H2O and 250 μLof ice-cold chloroform were added to induce phase separation. The lower organic phase was transferred to a fresh tube after centrifugation at 12 000 rpm for 5 min at 4 °C. A second round of extraction was performed via the addition of 450 μL of ice-cold chloroform to the remaining samples. The samples were vortexed and centrifuged at 12 000 rpm for 5 min at 4 °C. The organic extracts from both rounds of extraction were pooled and dried using SpeedVac (Genevac miVac, Tegent Scientifc Ltd., England) under OH mode for lipidomic analyses.

5.4 Animals

C57BL/6 N male and female mice were purchased from Beijing Vital River Laboratory Animal Technologies Co. Ltd. A 12-h light-dark cycle was maintained in the housing room. The temperature was set as 22 ± 1 °C. All mice had access to food and water ad libitum and each mouse was housed in a separate cage. All mice were fed with a standard AIN-93 M diet. For aging study, blood samples and organ/tissues were collected from 6-month old male (n = 2) and female (n = 3) mice, 12-month old male (n = 6) and female (n = 6) mice, 16-month old male (n = 6) and female (n = 6) mice, and 20-month old male (n = 5) and female (n = 6) mice. For measurement of mitochondrial oxygen consumption rate, proximal tubule epithelial cells were isolated from 9 mice with different ages. For CBE experiment, 4-month old female (n = 4) and 14-month old female (n = 4) mice were subjected to CBE treatment, 4-month old female (n = 4) and 14-month old female (n = 4) mice used as controls were treated with saline. All animal handling procedures were approved by Animal Care and Use Committee from Institute of Genetics and Developmental Biology, Chinese Academy of Sciences.

5.5 Lipid and metabolite extraction from mouse tissues

Lipids and metabolites were extracted from tissues using established protocols [92,93]. Briefly, 900 μL of chloroform:methanol (1:2, v/v) containing 10 % deionized H2O was added to each sample. Samples were homogenized on an automated bead ruptor (OMNI, Seattle, WA, USA). Following homogenization, samples were incubated at 1500 rpm at 4 °C for 1 h. At the end of the incubation, 400 μL of ice-cold deionized H2O and 300 μL of ice-cold chloroform were added to induce phase separation. The samples were centrifuged at 12 000 rpm for 5 min at 4 °C. The lower organic phase was transferred to a new tube. A second round of extraction was performed via adding 500 μL of ice-cold chloroform to the remaining aqueous phase. Samples were vortexed and centrifuged at 12 000 rpm for 5 min at 4 °C. The lower organic phases from both rounds of extraction were pooled and dried using SpeedVac under OH mode, and reconstituted in chloroform:methanol (1:1; v/v) for lipidomic analyses. Two rounds of extraction were performed to maximise lipid recovery. The remaining aqueous phase containing polar metabolites were centrifuged at 12 000 rpm for 5 min at 4 °C. Clean supernatant was transferred into a fresh 1.5 mL centrifuge tube and dried using a SpeedVac (Genevac miVac, Tegent Scientifc Ltd., England). The dried extracts were reconstituted using 2 % acetonitrile in water for metabolomics analyses.

5.6 Metabolomics

A Waters ACQUITY UPLC HSS T3 column (1.8 μm, 3.0 × 100 mm) (Waters, Dublin, Ireland) was used for separation of polar metabolites. Ultra-performance liquid chromatography (UPLC) (Agilent 1290 II) coupled to the Agilent 6546 LC/Q-TOF (Agilent Technologies, Germany) was used to acquire mass spectrometry (MS) data. The column oven temperature was maintained at 40 °C, and autosampler was set at 10 °C. The injector volume was 5 μL. Flow rate was 0.35 mL min−1. Mobile phase A comprised water containing 0.1 % FA (v/v), and mobile phase B was acetonitrile. The following linear gradient was used: 0–1.0 min with 2 % B, 1.0–6.0 min with 2%–42 % B, 6.0–8.0 min with 42%–65 % B, 8.0–10.0 min with 65%–76 % B, 10.0–11.0 min with 76%–98 % B, 11.0–15.0 min with 98%–98 % B20. Positive and negative polarity data was acquired on the Agilent 6546 LC/Q-TOF using full scan mode with ranges of m/z 60–1100. Data dependent acquisition using iterative exclusion was conducted via five repeated injections of pooled (QC) plasma sample for each polarity [94]. The MS parameters used for detection were: ESI source voltage +3500 or −3000 kV in positive and negative ion modes, respectively; drying gas temperature, 300 °C; nebulizer pressure, 38psi; nitrogen drying gas, 7L/min; sheath gas temperature and flow at 350 °C and 11L/min, respectively. MS acquisition rate was 2 spectra/sec and m/z data range from 60 to 1100 were stored in profile mode. Dynamic mass axis calibration was achieved by continuous infusion of a reference mass solution. A cocktail of isotopically-labeled internal standards purchased from the Cambridge Isotope Laboratories were added for metabolite quantitation, including L-Tryptophan-d8, l-Isoleucine-d10, l-leucine-d10, l-Methionine-d3, l-Valine-d8, l-Proline-d7, l-Alanine-d4, dl-Serine-d3, l-Glutamine-d5, l-Aspartic acid-d3, l-Arginine-d7, l-Glutamate-d5, l-Lysine-d9, l-Histidine-13C6, Taurine-13C2, Betaine-d11, Urea-(13C,15N2), l-lactate-d3, Trimethylamine N-oxide-d9, Choline-d13, Malic acid-d3, Citric acid-d4, Succinic acid-d4, Fumaric acid-d2, Hypoxanthine-d3, Xanthine-15N2, Thymidine (13C10,15N2), Inosine-15N4, Cytidine-13C5, Uridine-d2, Methylsuccinic acid-d6, Benzoic acid-d5, Creatine-d3, Creatinine-d3, Glutaric acid-d4, Hippuric acid-d5, Kynurenic acid-d5, l-Citrulline-d4, l-Threonine-(13C4, 15 N), l-Tyrosine-d7, P-cresol sulfate-d7, Sarcosine-d3, Trans-4-hydroxy-l-proline-d3, Uric acid-(13C; 15N3), Pyruvate-d3. Peak areas of endogenous metabolites were normalized to the areas of their corresponding isotopically-labeled structural analogues for quantitation [18,20]. For endogenous metabolites without labeled structural analogues, an automated algorithm selects the optimal internal standard for quantitation based on the rule of minimal coefficients of variations (COVs) after normalization [18]. All detected ions were extracted using the Agilent MassHunter software into Excel in the format of a two-dimensional matrix, including mass to charge ratio (m/z), retention time, and peak areas. MS/MS data were compared with spectra deposited in the Human metabolome database (HMDB), METLIN, and standard references to annotate metabolite identities.

5.7 Lipidomics

Analyses of polar lipids from murine tissue and organ lipid extracts were performed a Jasper HPLC coupled with Sciex Triple Quad 4500MD, whereas neutral lipids were analyzed on an Agilent 1260 HPLC connected to Sciex 5500 QTRAP, both under electrospray ionization mode. Methodological details were comprehensively reported in a recent publication [95]. Internal standard cocktail used for lipid quantification included d9-PC32:0(16:0/16:0), d9-PC36:1p(18:0p/18:1), d7-PE33:1(15:0/18:1), d9-PE36:1p(18:0p/18:1), d31-PS(16:0/18:1), d7-PG33:1(15:0/18:1), d7-PI33:1(15:0/18:1), d7-PA33:1(15:0/18:1), C14-BMP, d9-SM d18:1/18:1, Cer d18:1/d7-15:0, GluCer d18:1/8:0, d3-LacCer d18:1/16:0, Gb3-d18:1/17:0, SL-d18:1/17:0, d7-LPC 18:1, d7-LPE 18:1, LPA-C17:0, LPI-C17:1, LPS-C17:1, LPG-C17:1, DAG(16:0/16:0)-d5, DAG(18:1/18:1)-d5, S1P-d17:1 and Sph-d17:1 obtained from Avanti Polar Lipids, GM3 d18:1/18:0-d3 from Matreya LLC, TAG(14:0)3-d5, TAG(16:0)3-d5, TAG(18:0)3-d5, d6-CE18:0 and d6-cholesterol purchased from CDN Isotopes, and d3-16:0-carnitine from Cambridge Isotope Laboratories. d31-FFA-16:0 from Sigma-Aldrich and d8-FFA-20:4 from Cayman Chemicals were used for quantification of saturated/monounsaturated fatty acids and polyunsaturated fatty acids, respectively.

Polar lipids were separated on a TUP-HB Silica column (3 μm, i. d. 150 × 2.1 mm) under the following chromatographic conditions: mobile phase A (chloroform:methanol:ammonium hydroxide, 89.5:10:0.5) and mobile phase B (chloroform: methanol: water: ammonium hydroxide:, 27:65:7:1) at a flow rate of 300 μL/min and column oven temperature was at 30 °C.The gradient started with 2 % B and was held for 2 min, which was then increased to 85 % of B over 6 min, and was held at 85 % for 1 min before further increasing to 100 % B over 0.2 min. The gradient was maintained at 100 % B for 3.8 min before returning to 2 % B over 0.5 min, and was finally equilibrated at 2 % B for 3.5 min prior to the next injection. Glycerol lipids including DAGs and TAGs were quantified using a modified version of reverse phase HPLC/MRM. Separation of neutral lipids were achieved on a Phenomenex Kinetex-C18 column (2.6 μm, i. d. 4.6 × 100 mm) using an isocratic mobile phase containing chloroform:methanol:0.1 M ammonium acetate 100:100:4 (v/v/v) at a flow rate of 300 μL for 10 min. Levels of short-, medium-, and long-chain TAGs were calculated by referencing to spiked internal standards of TAG(14:0)3-d5, TAG(16:0)3-d5 and TAG(18:0)3-d5 obtained from CDN isotopes (Quebec, Canada), respectively. DAGs were quantified using d5-DAG16:0/16:0 and d5-DAG18:1/18:1 as internal standards from Avanti Polar Lipids. Free cholesterols and cholesteryl esters were quantitated in the atmospheric pressure chemical ionization mode using d6-Cho and d6-CE18:0 (CDN isotopes) as internal standards.

Analysis of GluCer in human plasma extracts were conducted on a Shimadzu Nexera Prominence LC coupled with Sciex QTRAP 7500. GluCer d18:1/8:0 from Avanti Polar Lipids was used as an internal standard for quantification. Separation of GluCer species was performed on a TUP-HB silica column (3 μm, i. d. 150 × 2.1 mm) under the following chromatographic conditions: mobile phase A (chloroform:methanol:ammonium hydroxide, 89.5:10:0.5) and mobile phase B (chloroform:methanol: ammonium hydroxide: water, 27:65:1:7) at a flow rate of 300 μL/min and column oven temperature at 35 °C. The gradient began with 5 % of B and was held for 4 min, which was then increased to 35 % of B over 1.5 min, and further increasing to 95 % B over 3 min. The gradient was maintained at 95 % B for 3.5 min before returning to 5 % B over 0.5 min, and was finally equilibrated for 4.5 min prior to the next injection.

5.8 Cell culture

HKC cell line was obtained from the American Type Culture Collection. Cells were cultured in a 1:1 mixture of Dulbecco's Modified Eagle's medium and Ham's F-12 nutrient mixture (DMEM/F12, Sigma-Aldrich) containing 10 % fetal bovine serum (Sigma-Aldrich), 50 U/mL penicillin, and 50 mg/mL streptomycin at 37 °C in an atmosphere containing 5 % CO2. The culture medium was changed every 2–3 days thereafter. Renal tubular epithelial cells from C57BL/6 N mice were grown on plates precoated with 20 mM acetic acid and 5 μg collagen type 1 (Thermo Scientific) in mouse renal tubular epithelial cells complete medium (Procell).

5.9 Primary culture of kidney proximal tubule epithelial cells

Mice were anesthetized, fixed supine and kidneys were harvested. Kidney cortexes were isolated, minced and digested in 1 mg/mL collagenase at 37 °C with shaking for 30 min, and filtered successively with 250 μm and 70 μm cell strainers. The filtrates were then centrifuged at 50×g for 5 min, and the cell pellet was seeded on plates precoated with 20 mM acetic acid and 5 μg collagen type 1 (Thermo Scientific) in mouse renal epithelial cell complete medium (Procell). On the following day, the culture medium was collected and centrifuged at 50×g for 4 min to pellet cells that had not attached, and the pelleted cells were suspended in fresh growth media and returned to the original culture plate. Proximal tubule epithelial cells were grown to confluence for 4–7 days, then used for subsequent experiments as previously described [96].

5.10 CBE administration

The GCase inhibitor conduritol-b-epoxide (CBE, MedChem Express) was reconstituted at 10 mg/mL in 0.9 % NaCl and stored at −20 °C. For cell culture experiments, vehicle or CBE were diluted to the final concentrations as indicated in the figure legends corresponding to individual experiments. For in vivo experiments, mice were injected intraperitoneally with 100 mg/kg CBE or an equivalent volume of 0.9 % NaCl daily for 21 days, with injection at alternating sides on each day. Mice were sacrificed at approximately 24 h following the final injection of CBE or vehicle.

5.11 Measurement of mitochondrial oxygen consumption rate (OCR)

A seahorse XFe96 extracellular flux analyzer (Agilent Technologies) was used to measure oxygen consumption rate (OCR). We evaluated bio-energetic fluxes using a Mito stress Test Kit (Agilent Technologies) according to the manufacturer's protocol. The assay was performed in Agilent Seahorse XF base medium (Agilent Technologies) containing 10 mM glucose, 2 mM glutamine, and 1 mM pyruvate. Mitochondrial function was evaluated by monitoring changes in the OCRs at baseline and after adding 2 μM oligomycin, 1 μM FCCP, and 0.5 μM rotenone/antimycin A. Profiles for mitochondrial function were calculated using the Wave software (Agilent Technologies) as per the manufacturer's instructions.

5.12 Immunoblot analysis

Kidneys were dissected and snap-frozen in liquid nitrogen. For cultured cells, cells were centrifuged, the medium was removed and the pellet was snap-frozen in liquid nitrogen. Proteins from tissues and cell pellets were extracted using RIPA lysis buffer (Meilunbio) with protease and phosphatase inhibitor cocktail (Meilunbio). The protein content was determined using a Pierce BCA protein assay kit (Thermo Fisher Scientific) according to the manufacturer's instructions. After boiling, lysates containing 40 μg protein were subjected to SDS–PAGE and transblotted onto nitrocellulose membranes (Pall). Primary antibodies included: anti-p53 (Cell Signaling, 2524S, 1:500), anti-OPA1 (abcam, ab42364, 1:1000), anti-DRP1 (Cell Signaling, 8570, 1:1000), anti-mTOR (Immunoway, YT2913, 1:1000), anti-p-mTOR (Abmart, T56571, 1:1000), anti-S6K (Cell Signaling, 9202S, 1:1000), anti-p-S6K (Cell Signaling, 9205S, 1:1000), anti-β-actin (Sigma, A5441, 1:1000), anti-TBC1D15 (Immunoway, YN3051, 1:1000), anti-pink1 (Santa Cruz, sc-517 353,1:50), anti-GAPDH (CUSABIO, CSB-MA000071M1m, 1:2000). Secondary antibodies included: goat anti-rabbit HRP (ZSGB-BIO, ZB-2305, 1:5000), goat anti-mouse HRP (ZSGB-BIO, ZB-2301, 1:5000). Chemiluminescence detection of proteins was performed with super sensitive ECL luminescence reagent (Meilunbio) and imaged with the Chemidoc Imaging System (Bio-Rad). Bands were quantified using ImageJ software.

5.13 Haematoxylin & eosin staining

Liver samples were fixed in formalin. After being embedded in paraffin, 5 μm sections were obtained for further staining as previously described [97]. Haematoxylin & eosin (H&E) staining was conducted for histological analysis. Images were taken with a Zeiss microscope.

5.14 Bioinformatics and statistical analyses

Metabolites peak areas were log-transformed and standardized to z-scores. For metabolite associations with aging, Pearson correlation was calculated with the corr. test function in the R package psych (v.2.1.6). The following linear model: metabolite level ∼ α +β1 age + ε was used to determine the eﬀect of age on individual metabolite levels. The type II sum of squares was calculated using the ANOVA function of the R package car (v.3.0–11). To estimate metabolite trajectories across aging, plasma metabolite levels were z scored, and locally estimated scatterplot smoothing (LOESS) regression was fitted for each plasma metabolite. To group metabolites with similar trajectories, pairwise differences between LOESS estimates were calculated on the basis of the Euclidian distance, and hierarchical clustering was performed using the complete method. R package clusterProfiler (v.4.10.0) was used for Over-Representation Analysis. KEGG, Wikipathway, Reactome databases are from R package graphite (v.1.44.0). All identified metabolites that can be mapped to specific HMDB IDs were used as the background database for enrichment analysis. To identify and quantify linear and non-linear changes of the plasma metabolites during aging, we used the DE-SWAN approach as described previously [36]. Clusters of co-abundant plasma metabolites and lipids were identified using the R package WGCNA as described previously [98,99]. Time course data was smoothed using loess function from R package. For Trans-omics integration of senescence-associated plasma proteome, metabolome and lipidome was performed using the R package Mfuzz to group proteins and metabolites exhibiting similar temporal patterns across aging into clusters. For the human proteome, lipidome and metabolome data, ages (years) were grouped into seven windows prior to the analysis: 25 (20–29), 35 (30–39), 45 (40–49), 55 (50–59), 65 (60–69), 75 (70–79) and 85 (80–89). Mouse proteome samples were collected at 6, 12, 15 and 20 months, and mouse lipidome samples were analyzed at 6, 12, 16 and 20 months. In order to align the time-points, predicted values based on loess model were obtained at 6, 12, 16 and 20 months prior to data integration. All the statistical analyses were performed using R software version 4.3.2.

CRediT authorship contribution statement

Ke Zheng: Writing – review & editing, Project administration, Funding acquisition, Conceptualization. Yujun Qian: Writing – review & editing, Writing – original draft, Formal analysis. Haiyun Wang: Project administration. Dan Song: Project administration. Hui You: Data curation. Bo Hou: Data curation. Fei Han: Data curation. Yicheng Zhu: Data curation. Feng Feng: Data curation. Sin Man Lam: Writing – review & editing, Formal analysis, Conceptualization. Guanghou Shui: Supervision, Funding acquisition, Conceptualization. Xuemei Li: Supervision, Funding acquisition, Conceptualization.

Declaration of competing interest

The authors have declared no conflict of interest.

Appendix A Supplementary data

The following are the Supplementary data to this article.Multimedia component 1

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Data availability

Lipidomics datasets of plasma and erythrocytes, and proteomics datasets of erythrocytes were provided as supplementary tables.

Acknowledgements

This research was funded by the National High Level Hospital Clinical Research Funding [2022-PUMCH-B-021], 12th National Science and Technology Support Program (Grant Number 2011BAI10B02), Beijing Municipal Science & Technology Commission Program (Grant Number D181100000118001), National Key R&D Program of China (2018YFA0800901) and National Natural Science Foundation of China (92057202, 32321004, 92357308).

Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.redox.2024.103295.
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