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The Journal of Clinical Endocrinology and Metabolism logoLink to The Journal of Clinical Endocrinology and Metabolism
. 2022 Sep 19;107(12):3231–3241. doi: 10.1210/clinem/dgac535

Aging Impairs Adaptive Unfolded Protein Response and Drives Beta Cell Dedifferentiation in Humans

Jiaxi Song 1,2,#, Qicheng Ni 3,4,#, Jiajun Sun 5,6, Jing Xie 7, Jianmin Liu 8,9, Guang Ning 10,11, Weiqing Wang 12,13,✉, Qidi Wang 14,15,16,✉
PMCID: PMC9693768  PMID: 36125175

Abstract

Context

Diabetes is an age-related disease; however, the mechanism underlying senescent beta cell failure is still unknown.

Objective

The present study was designed to investigate whether and how the differentiated state was altered in senescent human beta cells by excluding the effects of impaired glucose tolerance.

Methods

We calculated the percentage of hormone-negative/chromogranin A–positive endocrine cells and evaluated the expressions of forkhead box O1 (FoxO1) and Urocortin 3 (UCN3) in islets from 31 nondiabetic individuals, divided into young (<40 years), middle-aged (40-60 years) and elderly (>60 years) groups. We also assessed adaptive unfolded protein response markers glucose-regulated protein 94 (GRP94), and spliced X-box binding protein 1 (XBP1s) in senescent beta cells and their possible contributions to maintaining beta cell identity and differentiation state.

Results

We found an almost 2-fold increase in the proportion of dedifferentiated cells in elderly and middle-aged groups compared with the young group (3.1 ± 1.0% and 3.0 ± 0.9% vs 1.7 ± 0.5%, P < .001). This was accompanied by inactivation of FoxO1 and loss of UCN3 expression in senescent human beta cells. In addition, we demonstrated that the expression levels of adaptive unfolded protein response (UPR) components GRP94 and XBP1s declined with age. In vitro data showed knockdown GRP94 in Min6-triggered cells to dedifferentiate and acquire progenitor features, while restored GRP94 levels in H2O2-induced senescent Min6 cells rescued beta cell identity.

Conclusion

Our finding highlights that the failure to establish proper adaptive UPR in senescent human beta cells shifts their differentiated states, possibly representing a crucial step in the pathogenesis of age-related beta cell failure.

Keywords: ageing, beta cell dedifferentiation, type 2 diabetes, unfolded protein response

Type 2 diabetes is an age-related disease that is accompanied by progressive beta cell failure to secrete adequate insulin to meet the increased insulin demand under metabolic stress (1–6). Previous studies have suggested that insulin resistance increases with age; however, insulin resistance alone does not account for age-related glucose intolerance (7, 8). Several human studies have consistently found an age-dependent decline of beta cell function, independent of insulin resistance (9–11). Age-related changes in beta cell proliferation capacity (12) and components of stimulus–secretion coupling (13, 14) have been studied as contributors to senescent beta cell dysfunction. Recently, Aguayo-Mazzucato et al sorted senescent (beta-gal+) beta cells from 7-8–month-old mice and found decreased beta cell identity (Insulin 1, MafA, Nkx6.1, and Pdx1) and increased disallowed genes (Ldha and Catalase) compared with nonsenescent (beta-gal−) beta cells (15). More importantly, a single-cell transcriptome analysis performed on human pancreas from 8 donors spanning 6 decades of life revealed that islet endocrine cells from older donors displayed increased levels of transcriptional noise and expressed more cell-atypic hormone mRNA (16). Such “fate drift” is emblematic of age-dependent transcriptional instability and endocrine cell reprogramming, which might contribute to beta cell failure during aging.

Beta cell dedifferentiation is considered as a novel mechanism of beta cell failure, in which beta cells lose their identity and differentiated state, revert to a progenitor-like stage, and even reprogram to another endocrine cell fate (17). Beta cell dedifferentiation was first reported in beta cell–specific forkhead box O1 (FoxO1)–deficient mice upon ageing and multiparous challenges (18), and was also observed in islets from other mouse models of obesity and type 2 diabetes (19–22). Direct evidence of human beta cell dedifferentiation in diabetes was provided by independent research groups, demonstrating a significant increase in hormone-negative/chromogranin A–positive endocrine cells in islets from individuals with type 2 diabetes (23, 24). In addition, our previous studies reported an increased rate of beta cell dedifferentiation not only in well-controlled type 2 diabetes (24) but also in diseases with increased susceptibility to diabetes, such as chronic pancreatitis (24) and pancreatic cancer (25). Interestingly, a recent study revealed that the gene profiles in senescent rodent beta cells were similar to those of the dedifferentiated beta cells. Many of these age-related changes were present both in glucose-intolerant C57BL/6J and in normoglycemic mice model (INKATTAC) (26). It is currently unknown whether and how ageing, a risk factor of type 2 diabetes, influences the differentiated state of human beta cells before blood glucose rises.

In the present study, we investigated the role of aging on beta cell dedifferentiation in nondiabetic subjects and explored the possible involvement of adaptive unfolded protein response (UPR) in age-related beta cell failure. Our data aim to provide clues for high susceptibility to type 2 diabetes in elderly subjects and candidates targeting age-related beta cell failure.

Materials and Methods

Patients and Human Pancreas

A total of 2065 subjects with partial pancreatectomy for various reasons performed in the Department of Surgery in Ruijin Hospital between January 2013 and August 2017 were recruited. By reviewing pathology diagnosis and CA19-9 levels, subjects who had been reported as having a malignant tumor were excluded. Cases were enrolled in the current study according to the following criteria: (1) pathological diagnosis of pancreatic serous cystadenoma or pancreatic mucinous cystadenoma; (2) nondiabetes defined as fasting blood glucose (FBG) ≤ 5.5 mmol/L according to American Diabetes Association guidelines (27); (3) nonobesity, defined as a body mass index (BMI) less than 30 kg/m2 according to American Association of Clinical Endocrinologist Medical Guidelines (28); (4) no history of diabetes and chronic pancreatitis; (5) adults with age >18 years old; and (6) with a complete dataset, including clinical characteristics, medical history, and laboratory tests.

A total of 31 nondiabetic patients were included in and divided into 3 different age groups, in other words, young group (<40 years), middle-aged group (40-60 years), and elderly group (>60 years) (Table 1 (29)). Paraffin sections of pancreatic tissues far from the margin of the pancreatectomy were obtained from the Department of Pathology in Ruijin Hospital for subsequent analysis. All of these tissues were reverified to ensure the final pathology diagnosis by a pathologist.

This study was approved by the Institutional Review Board of the Ruijin Hospital affiliated to Shanghai Jiao Tong University School of Medicine and was in accordance with the principles of the Declaration of Helsinki.

Immunostaining

Human pancreas samples were fixed and processed for immunohistochemistry according to the standard protocol, as previously described (30). Briefly, after being deparaffinized and rehydrated, antigen retrieval was performed by heating slides in antigen-unmasking solution (H-3300, Vector). All slides were incubated with primary antibodies and diluted in Dako Antibody Diluent (Dako; Burlington Canada) at 4°C overnight. The dilutions were as follows: guinea pig anti-insulin (1:10, Agilent Cat# IR002, RRID:AB_2800361), rabbit antiglucagon (1:200, Abcam Cat# ab10988, RRID:AB_297642), rabbit antisomatostatin (1:400, Millipore Cat# AB5494, RRID:AB_2255374), rabbit antipancreatic polypeptide (1:400, Millipore Cat# AB939, RRID:AB_92383), mouse antichromogranin A (1:100, Millipore Cat# MAB5268, RRID:AB_11213294), rabbit anti-UCN3 (1:200, Sigma-Aldrich Cat# HPA038281, RRID:AB_10672408), rabbit anti-FoxO1 (1:200, Cell Signaling Technology Cat# 2880, RRID:AB_2106495), rat anti-GRP94 (1:250, Thermo Fisher Scientific Cat# MA3-016, RRID:AB_2248666), rabbit anti-XBP1s (1:200, ABclonal Cat# A17007, RRID:AB_2772919), rabbit anti-ATF4 (1:100, Proteintech Cat# 10835-1-AP, RRID:AB_2058600), rabbit anti-ATF6 (1:200, Proteintech Cat# 24169-1-AP, RRID:AB_2876891), and rabbit anti-BIP (1:100, Proteintech Cat# 11587-1-AP, RRID:AB_2119855). Primary antibodies were detected using Alexa Fluor 488, 594, and 647 (1:500, Jackson Immuno Research Laboratories) as secondary antibodies. Nuclei were stained with 4,6-diamidino-2-phenylindole (DAPI, Vector Laboratories; Burlingame, CA).

Senescence-associated beta-galactosidase (SA-beta-gal) staining of Min6 cells was performed using an SA-beta-gal staining kit (Beyotime Biotechnology, China, C0602) according to the manufacturer’s protocol.

Image Acquisition and Analysis

We captured immunofluorescence images using a Zeiss LSM 880 confocal microscope. We performed the quantifications blindly using ImageJ software (National Institutes of Health, Bethesda, MD, USA) to analyze individual cells in whole-slide images; at least 2 random sections and 818.1 ± 174.7 cells per donor were scored. We counted the Chromogranin A (CGA)-positive cells representing endocrine-derived cells first. CGA-positive and hormone-negative cells were identified as dedifferentiated cells, and insulin-positive and other pancreatic hormone–positive cells as multihormone cells. Fluorescent intensity was quantified blindly using ImageJ software (National Institutes of Health, Bethesda, MD, USA) with the EzColocalization plugin according to the protocol described by Stauffer et al (31).

Cell Culture

Min6 cells (Mouse insulinoma cells) were purchased from CAMS Cell Culture Center (Beijing, China), grown in Dulbecco’s modified Eagle’s medium (Gibco) containing 25.0 mmol/L glucose, 15% fetal bovine serum, 100 IU/mL penicillin, 100 µg/mL streptomycin, 10.2 mM L-glutamine, and 2.5 mM beta-mercaptoethanol at 37°C in a humidified 5% CO2 atmosphere. As for induction of senescence, Min6 cells were passaged and plated in 12-well cell culture plates for 24 hours. Cells were treated with 200 µM H2O2 for 48 hours and replaced by normal media for another 24 hours to generate senescence. Short hairpin RNA (shRNA) lentivirus targeting GRP94, XBP1, and control were constructed, packaged, purified, and titrated at GeneChem Co. Ltd. Min6 cells were infected with purified lentivirus at 50 multiplicity of infection for 48 hours. Overexpression constructs for mouse GRP94-Flag and Flag control plasmid transfection were conducted using Lipofectamine 3000 (Invitrogen) according to the manufacturer's manual.

Extraction of RNA and Quantitative Real-Time Polymerase Chain Reaction Analysis

Total cell RNA was extracted by Eastep® Super Total RNA Extraction Kit (Promega, LS1040) according to the manufacturer's protocols, and synthesis of cDNA was used with PrimerScript Master Mix (Takara). Duplicate samples for quantitative polymerase chain reaction were carried out using TB Green Premix Ex Taq (Takara) and performed with ICycler (ABI, USA). Primer sequences can be obtained elsewhere (Table 2 (29)).

Immunoblot Analysis

Min6 cells were lysed, quantified, and blotted as described before (30). Primary antibodies were listed as follows: rat anti-GRP94 (1:4000, Cell Signaling Technology Cat# 20292, RRID:AB_2722657); rabbit anti-XBP1s (1:1000, ABclonal Cat# A17007, RRID:AB_2772919). Horse radish peroxidase–conjugated alpha Tubulin (1:5000, Proteintech Cat# HRP-66031, RRID:AB_2687491) was used as an internal control to normalize band intensity.

Statistical Analysis

Data are presented as the mean ± SD or mean (minimum–maximum) unless otherwise noted. Statistical comparisons among the groups were performed using analysis of Fisher's exact tests for categorical variables, 1-way analysis of variance with Bonferroni's post hoc test or unpaired 2-tailed Student's t-test for continuous variables. Correlation coefficients were calculated by simple and multiple regression analyses. P < .05 was considered statistically significant. All statistical analyses were conducted using SPSS version 24.0 statistical software (IBM, Armonk, New York).

Results

Increase in Dedifferentiated Cells in Nondiabetic Senescent Human Islets

To explore whether beta cells underwent dedifferentiation during their progression to ageing, we collected pancreas samples from 31 nondiabetic individuals with age range 25-94 years, and divided them into young (<40 years, range 25-40 years), middle-aged (40-60 years, range 48-59 years), and elderly (>60 years, range 60–94 years) groups. The fasting blood glucose levels were comparable among the 3 groups (4.9 ± 0.5 vs 5.1 ± 0.3 vs 5.0 ± 0.6 mM, elderly vs middle-aged vs young, P > .05; Table 1) and were all within normal range according to American Diabetes Association guidelines (27). Subjects with history of diabetes, chronic pancreatitis, or pancreatic ductal adenocarcinoma, which had been reported to induce beta cell dedifferentiation (23–25), were excluded from the present study. All the subjects remained in the nonobesity range (28), and elderly subjects showed slightly increased BMI compared with young and middle-aged subjects (Table 1).

Table 1.

Characteristic of the study subjects

Subject profile Young (n = 10) Middle-aged (n = 10) Elderly (n = 11) P value
DM/CP/PDAC history No No No /
Age (years) 33 (25-40) 54 (48-59) 72 (60–94) <.001
Female, n (%) 8 (80.0%) 8 (80.0%) 4 (36.4%) .057
BMI (kg/m2) 19.9 ± 2.8 22.5 ± 1.9 23.4 ± 2.9 .011
FBG (mmol/L) 5.0 ± 0.6 5.1 ± 0.3 4.9 ± 0.5 .689
Smoking, no. (%) 1 (10%) 0 3 (27%) .286
% Dedifferentiation 1.7 ± 0.5 3.0 ± 0.9 3.1 ± 1.0 <.001
% Multihormone 2.5 ± 1.3 2.6 ± 1.1 2.9 ± 1.3 .816
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All continuous parameters are summarized by means ± SD or mean (minimum–maximum). P values were calculated from 1-way analysis of variance with Bonferroni's post hoc test for continuous variables, and Fisher's exact test for categorical variables.

Abbreviations: BMI, body mass index; CP, chronic pancreatitis; DM, diabetes mellitus; FBG, fasting blood glucose; PDAC, pancreatic ductal adenocarcinoma.

We first assessed the dedifferentiated cells and multihormone cells in pancreatic islets of all the subjects. As previously described (24, 25), the dedifferentiated cells were referred to as CGA-positive/hormone-negative cells (white arrows in Fig. 1A-1C), whereas multihormone cells were defined as coexpressing insulin and Gcg/Sst/Pp (yellow arrows in Fig. 1A (29)). Consistent with our previous reports (24, 25), regardless of age, the mean ratio of dedifferentiated cells was 2.6% (ranging from 1.2% to 4.8%) in human islets from 31 nondiabetic subjects. Interestingly, an almost 2-fold increase in the proportion of dedifferentiated cells was detected in middle-aged and elderly groups compared with the young group (3.1 ± 1.0% vs 3.0 ± 0.9% vs 1.7 ± 0.5%, elderly vs middle-aged vs young, P < .001; Fig. 1D and Table 1). We further calculated the ratio of dedifferentiated cells in different portions of the pancreas, namely, the head, body, and tail of the pancreas: there is a tendency for dedifferentiated cells to increase in the middle-aged and elderly groups compared with the young group, but this did not achieve statistical significance due to limited sample size (Fig. 1A and 1C (29)). We did not detect significant differences in the ratio of multihormone cells among the 3 groups (Fig. 1E). These data indicate that the ratio of dedifferentiated cells increased during aging.

Figure 1.

Figure 1.

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Representative immunofluorescence images of dedifferentiated cells from nondiabetic islets. (A-C) Immunofluorescence on pancreatic sections from young (A, case 867), middle-aged (Bb, case 263) and elderly (C, case 005) groups using insulin (white), cocktail of Gcg/Sst/Pp (red), CGA (green), and Dapi (blue) are shown. Boxes show higher magnification of the indicated region in the islets with white arrows demonstrating the dedifferentiated cells. (D-E) Quantitative analysis of dedifferentiated cells/CGA-positive cells and multihormone cells/CGA-positive cells. Scale bars, 20 μm. All data are expressed as mean ± SD. n = 10 in young group, n = 10 in middle-aged group, n = 11 in elderly group. **P < .01.

We then performed a univariate correlation analysis between the percentage of dedifferentiated cells in all subjects and their clinical parameters. We found the percentage of dedifferentiated cells exhibited a significantly positive correlation with age (r = 0.499, P = .004; Fig. 2A), but not with BMI (r = 0.319, P = .080; Fig. 2B) or FBG (r = −0.010, P = .957; Fig. 2C). Furthermore, according to multiple regression analyses, age (β = .027, P = .017; Fig. 2D), but not BMI and FBG, was independently and positively associated with the percentage of dedifferentiated cells.

Figure 2.

Figure 2.

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Correlation between the ratio of dedifferentiated cells and clinical parameters. (A-C) The Pearson correlations of the ratio of dedifferentiated cells with age (A), BMI (B), and FBG (C) in nondiabetic islets. (D) Multivariate association of the clinical variable with the ratio of dedifferentiated cells in nondiabetic islets. The r of Pearson correlation and P values are shown in each panel. *P < .05 was considered statistically significant.

FoxO1 Inactivation and UCN3 Reduction Were Observed in Nondiabetic Senescent Human Islets

FoxO1 is an important transcription factor to enforce beta cell fate under physiologic stress including ageing (18, 32, 33). Translocation of FoxO1 from the nucleus to the cytoplasm decreases its activity (33). In the present study, we found at least 20% of insulin-positive cells had nuclear FoxO1 staining in young subjects, whereas FoxO1 expression was mostly detectable in the cytoplasm of beta cells from middle-aged and elderly subjects (Fig. 3A). We further calculated the number of beta cells with nuclear or cytoplasmic FoxO1 expression and found a robust reduction in fractions of beta cells with nuclear FoxO1 expression in middle-aged and elderly subjects compared with young ones (1.0 ± 1.2% vs 4.7 ± 2.8% vs 21.2 ± 8.8%, elderly vs middle-aged vs young, P < .001; Fig. 3B). Moreover, the percentage of beta cells with nuclear FoxO1 expression was negatively related with age (r = –0.764, P = .004; Fig. 3B).

Figure 3.

Figure 3.

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Expression of FoxO1 and UCN3 in nondiabetic elderly individuals. (A, C) Immunofluorescence images of pancreatic sections are shown with FoxO1 or UCN3 (red), insulin (green) and Dapi (blue) in young, middle-aged, and elderly groups. (B) Quantitative analysis of subcellular localization of FoxO1 in young, middle-aged, and elderly groups; correlation of quantified nuclear FoxO1 expression with age. (D) Quantification analysis of UCN3 intensity adjusted with islet area in young, middle-aged, and elderly groups; correlation of quantified UCN3 intensity with age. Scale bars, 20 μm. Data present means ± SD. The r of Pearson correlation and P values are shown in each panel. n = 4, *P < .05.

It is known that reduction of beta cell functional marker UCN3 (urocortin 3) is an early event during beta cell dedifferentiation in diabetes (34, 35). We performed immunofluorescent staining for UCN3 and insulin on the pancreas from the young, middle-aged, and elderly subjects, and found a remarkable decline in UCN3 expression in middle-aged and senescent human beta cells (43.7 ± 16.7 vs 31.1 ± 15.0% vs 80.9 ± 24.8, elderly vs middle-aged vs young, P < .05; Fig. 3C and 3D). Furthermore, quantified UCN3 intensity was negatively correlated with age (r = −0.742, P = .009; Fig. 3D).

Decreased Adaptive UPR in Nondiabetic Senescent Human Beta Cells

The accumulation of unfolded/misfolded proteins is a hallmark of ageing, and the potential of adaptive UPR to resolve endoplasmic reticulum (ER) stress has been implicated as a key mechanism of (de)compensatory beta cells (36, 37). In the present study, we found the expression level of GRP94 (glucose-regulated protein 94), a chaperone involved in adaptive UPR to execute protein quality control (38), was remarkably diminished in middle-aged and senescent human beta cells (19.9 ± 9.6 vs 25.2 ± 6.2 vs 57.3 ± 16.5, elderly vs middle-aged vs young, P < 0.01; Fig. 4A and 4B). Moreover, the quantified expression level of GRP94 was negatively associated with age (r = −0.643, P = .005; Fig. 4B). We then assessed another adaptive UPR marker XBP1s (spliced X-box binding protein 1), (39) and found XBP1s expression was also significantly decreased in senescent human beta cells (28.2 ± 15.8 vs 59.2 ± 32.0, elderly vs young, P < .05; Fig. 2A and 2E (29)). Quantified expression levels of XBP1s also exhibited a negative correlation with age (r = −0.654, P = .015; Fig. 2E (29)). However, we did not detect differences in expression levels of ATF4, ATF6, and BIP (Fig. 2B-D, F-H (29)). The above data indicate that failure to establish adaptive UPR might contribute to defects in senescent human beta cells.

Figure 4.

Figure 4.

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Attenuated GRP94 expression in nondiabetic elderly individuals influences beta cell identity. (A) Representative images of pancreatic section staining for GRP94 (red), insulin (green), and Dapi (blue) in young, middle-aged, and elderly groups. (B) Quantitative analysis of GRP94 intensity in young, middle-aged, and elderly groups; correlation of quantified GRP94 expression with age. (C) Immunoblot showed expression of GRP94 protein in ShGRP94 cells; band intensities normalized based on the corresponding TUBULIN intensity (n = 3). (D) Relative mRNA expression of transcription factors, genes involved in beta cell function, and progenitor cells in shGRP94 cells (n = 4). Scale bars, 20 μm. Data present means ± SD. The r of Pearson correlation and P values were shown in each panel. n = 7 in the young group, n = 4 in Middle-aged group, n = 6 in Elderly group. *P < .05, **P < .01, ***P < .001.

To examine the possible link between adaptive UPR and beta cell identity, we treated Min6 cells with GRP94 lentiviral shRNA for 48 hours. Successful knockdown of GRP94 (Fig. 4C) significantly decreased mRNA expression of pancreatic and duodenal homeobox1 (Pdx1), Ucn3, and Neuronal Differentiation 1 (NeuroD1) (Fig. 4D). Likewise, decreased expression of Pdx1 and V-maf musculoaponeurotic fibrosarcoma oncogene family protein A (MafA) were detected in shXBP1 Min6 cells (Fig. 3A and B (29)). Interestingly, genes highly expressed in progenitor cells, including octamer-binding transcription factor 4 (Oct4) and neurogenin 3 (Ngn3) were upregulated in shGRP94 and shXBP1 Min6 cells (Fig. 4D; Fig. 3B (29)). These results suggest that decrease in adaptive UPR (GRP94 or XBP1s) in senescent beta cells might trigger identity loss and acquisition of progenitor-like properties.

Adaptive UPR (GRP94) Mediates H2O2-induced Senescent Beta Cell Dedifferentiation

To investigate the correlation between adaptive UPR and beta cell identity in senescent murine beta cells, we treated Min6 cells with 200 µM H2O2 for 48 hours (Fig. 5A). Induction of senescence was evidenced by increased beta-gal activity (Fig. 5B), upregulation of Cdkn1a expression (Fig. 5C), and induction of senescence-associated secretory profile factors, namely, Cxcl1, Cxcl5, Cxcl10, Ccl3, Cxcl12, and Ccl4 (Fig. 5D). H2O2-induced senescent Min6 cells had comparable mRNA levels of Xbp1 (Fig. 3D (29)) but diminished GRP94 expression at both mRNA and protein levels (Fig. 5E; Fig. 3C (29)). H2O2-treated Min6 cells shifted their differentiated state, with decreased expression of key genes vital to beta cell identity, namely, Pdx1, MafA, and Glut2, and increased levels of progenitor markers, namely, Oct4 and Ngn3 (Fig. 5G).

Figure 5.

Figure 5.

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Overexpression of GRP94 partially prevents age-induced beta cell dedifferentiation in Min6 cells. (A) Flow chart of inducing senescence with/without overexpressing GRP94 in Min6 cells. Senescence was induced by treating with 200 µM H2O2 for 48 hours, followed by normal media for another 24 hours. (B-D) Beta-gal+ activity (B), relative mRNA expression of Cdkn1a (C), and senescence-associated secretory profile (SASP) factors (D) in H2O2-treated Min6 cells. (E) Protein levels of GRP94 in H2O2-treated Min6 cells; band intensities normalized on the basis of the corresponding TUBULIN intensity (n = 3). (F) Immunoblot shows expression of GRP94 protein in GRP94-OE cells; band intensities normalized on the basis of the corresponding TUBULIN intensity (n = 3). (G) Relative mRNA expression of transcription factors, genes involved in beta cell function, and progenitor cells in H2O2-treated Min6 cells with/without overexpressing GRP94. (H) Summary of the failure of establishing proper adaptive UPR in senescent beta cells triggering their dedifferentiation. n = 6. Data present means ± SD. *P < .05, **P < .01, ***P < .001.

To evaluate whether diminished GRP94 expression mediated H2O2-induced senescent beta cell dedifferentiation, we transfected senescent Min6 cells with a GRP94 flag plasmid to overexpress GRP94 during H2O2 treatment (Fig. 5F). Interestingly, restored GRP94 expression in senescent Min6 cells significantly reversed H2O2-induced downregulation of beta cell identity genes Pdx1 and MafA, and upregulation of progenitor genes Oct4 and Ngn3 (Fig. 5G). The above data provide evidence that impaired adaptive UPR during aging might be responsible for beta cell identity loss and acquisition of progenitor-like properties.

Discussion

The incidence and prevalence of type 2 diabetes increase with age (2, 4, 5). Age-related deterioration in glucose tolerance is attributed to reduced glucose-stimulated insulin secretion (11, 13, 14, 40) and/or decreased insulin sensitivity in peripheral tissues (9, 41). It is documented that beta cell identity loss is an important mechanism of type 2 diabetes. Recently, Aguayo-Mazzucato et al have reported that senescent beta cells in aging mice (7-8 months C57BL/6J) display a distinctive transcription profile characterized by downregulation of hallmark beta cell genes (including Insulin 1, Mafa, Nkx6.1, and Pdx1) and expression of disallowed genes, such as Ldha and catalase (15). It remains unclear whether the human beta cell differentiation state is also affected in elderly subjects before diabetes occurs, and therefore contributes to increased susceptibility to type 2 diabetes.

Consistent with our previous reports (24, 25), the mean ratio of dedifferentiated beta cells in nondiabetic human islets ranged from 1.2% to 4.8%. For the first time, we have provided evidence for age-related increase in dedifferentiated cells in nondiabetic humans. First, we found a 2-fold increase in the ratio of dedifferentiated cells in middle-aged (40-60 years) and elderly (>60 years) subjects compared with young adults (20-40 years). Second, age was independently and positively associated with the percentage of dedifferentiated cells in humans. Third, we observed subcellular changes of FoxO1 from the nucleus to the cytoplasm in senescent human beta cells, which is known to cause beta cell dedifferentiation (32, 42, 43). Our finding of age-dependent FoxO1 subcellular change reinforced a possible link between FoxO1 inactivation and beta cell dedifferentiation in the degeneration of stress response during aging (44). Fourth, a significant decrease in the expression of UCN3, which is closely related to insulin release (34, 45, 46), was detected in human beta cells from elderly subjects. The detection of early decrease in UCN3 expression in beta cells from nondiabetic elderly subjects provided evidence that senescent human beta cells are prone to identity loss and beta cell dysfunction. It has been proposed that mechanisms including glucotoxicity and its downstream pathways such as hypoxia (47, 48), oxidative stress (35, 49–51), inflammation (52), and ER stress (53–55) are involved in beta cell dedifferentiation. Loss of ER homeostasis and subsequent accumulation of unfolded and misfolded proteins have been proved to be a central molecular hallmark of aging and many degenerative diseases, including diabetes (36). ER homeostasis is balanced by adaptive UPR to resolve ER stress and maladaptive UPR leading to apoptosis (56, 57). It has been shown that adaptive UPR of beta cells was decreased in rodent diabetic models and human type 2 diabetes (58). Recently, several rodent studies have attributed the failure of beta cell adaptive UPR induction (ER chaperones and ER stress sensors) to the abnormalities of beta cell gene expression and diabetes progression (59–61). In the present study, we detected a robust reduction in adaptive UPR markers, namely, GRP94 and XBP1s, in beta cells from elderly subjects. GRP94 is an ER chaperone protein involved in UPR to execute protein quality control (38), loss of which leads to impaired islet development in mice (62). Meanwhile, another component of adaptive UPR, a transcriptionally active spliced form of XBP1 (XBP1s), is also required to maintain mature beta cell identity in mice (60, 63). Decreased expression of XBP1s was observed in islets of patients with type 2 diabetes (64). In the current study, we found knockdown of adaptive UPR components GRP94 or XBP1 in intact murine beta cells led to significant downregulation of key genes vital to beta cell identity (Pdx1, Ucn3, and MafA) and upregulation of progenitor markers (Oct4 and Ngn3). Furthermore, by using a chemically induced senescence cell model, we reproduced age-related human beta cell dedifferentiation in vitro, with decreased GRP94 expression, downregulation of beta cell identity genes (Pdx1, MafA, and Glut2), and upregulation of progenitor genes (Oct4 and Ngn3). Importantly, when GRP94 expression was restored in H2O2-treated Min6 cells, beta cell identity loss was at least partly rescued.

It should be noted that all subjects involved in this study had no history of diabetes with normal fasting glucose levels less than 5.5 mmol/L, thus it is difficult to investigate whether the proportion of beta cell dedifferentiation will lead to changes in fasting glucose levels. We should also notice that the proportion of dedifferentiated beta cells is relatively low, even in elderly subjects (∼3%). Thus, the clinical significance of age-related beta cell dedifferentiation on the development of diabetes remains unclear. As Saisho et al previously reported that beta cell mass and apoptosis were unchanged in nondiabetic elderly population, whereas the mean individual beta cell cross-sectional area and beta cell nuclear diameter were both increased with age (65), indicating beta cell (de)compensation instead of beta cell loss is the main phenotypic change during the process of ageing. This finding actually supports our in vivo and in vitro data that UPR defects and beta cell dedifferentiation appear to be one of the underlying mechanisms of age-related beta cell failure.

To our knowledge, this is the first study showing that age-related defects in adaptive UPR might contribute to beta cell identity loss and acquisition of progenitor-like properties. Our findings highlight that the failure to establish proper adaptive UPR (GRP94/XBP1s) in senescent human beta cells might shift their differentiated state by reducing identity-related genes and reverting to a progenitor-like state, thus supporting predisposition of beta cell senescence in diabetes risk under the metabolic stress (Fig. 5H). Understanding the phenotypic change in senescent human beta cells would help us to search for potential targets for age-related beta cell failure.

Acknowledgments

We thank Ying Huang and all members of the Core Facility of Basic Medical Sciences of SJTU for their technical support. Figure 5H was modified from Servier Medical Art (http://smart.servier.com/), licensed under a Creative Common Attribution 3.0 Generic License. (https://creativecommons.org/licenses/by/3.0/)

Abbreviations

BMI

body mass index

FBG

fasting blood glucose

FoxO1

forkhead box O1

UPR

unfolded protein response

UCN3

Urocortin 3

Contributor Information

Jiaxi Song, Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.

Qicheng Ni, Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.

Jiajun Sun, Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.

Jing Xie, Department of Pathology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.

Jianmin Liu, Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.

Guang Ning, Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.

Weiqing Wang, Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.

Qidi Wang, Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Sino-French Research Center for Life Sciences and Genomics, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.

Financial Support

This work was supported by the National Natural Sciences Foundation of China (81870527, 82070795, 82100835, 91857205, 81730023) and the Shanghai Sailing Program (21YF1426900).

Author Contributions

J.X.S. designed and performed the experiments, analyzed the data, and wrote the manuscript. Q.C.N. contributed to conception, interpretation of data, and revised the manuscript. J.J.S. collected human samples. J.X. collected human samples and ensured the final pathology diagnosis of all tissues. G.N. and J.M.L. contributed to the discussion. W.Q.W. supervised the project and contributed to writing the manuscript. Q.D.W. conceived the project, and wrote and revised the manuscript. All authors critically reviewed and approved the final version of the manuscript. W.Q.W and Q.D.W. take responsibility for the integrity of the data and the accuracy of the data analysis.

Disclosures

The authors have nothing to disclose.

Data Availability

All data generated or analyzed during this study are included in the article. Further information about the data are available from the corresponding author upon request.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

All data generated or analyzed during this study are included in the article. Further information about the data are available from the corresponding author upon request.

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