Skip to main content
NCBI home page
Journal of Extracellular Biology logoLink to Journal of Extracellular Biology
. 2026 Aug 29;5(9):e70177. doi: 10.1002/jex2.70177

Calpeptin Reduces Extracellular Vesicle Secretion and Induces Anti‐Inflammatory Effects but Upregulates HO‐1 and Alters Adiponectin Levels in Human Adipocytes

Johanna Matilainen 1,2,✉, Sanni Foster 2, Viivi Berg 2,3, Janne Tampio 4, Tanja Turunen 2,5, Anne‐Mari Mustonen 2,6, Heikki Kyykallio 2, Maija Vaittinen 7, Ville Männistö 8, Jussi Pihlajamäki 7,9, Marjo Malinen 6,10, Sanna Oikari 2, Pirjo Käkelä 11,12, Dorota Kaminska 13, Veera Luukkonen 2,14, Kristiina M Huttunen 4, Martin Wabitsch 15,16, Petteri Nieminen 2, Kirsi Rilla 2,4
PMCID: PMC13525864  PMID: 42668896

ABSTRACT

Recent studies have shown that adipose tissue (AT) secretes elevated levels of extracellular vesicles (EVs) in obesity, and these EVs play roles in metabolic diseases. The inhibition of calpains has anti‐inflammatory and anti‐fibrotic effects on AT in mice and reduces EV secretion in some cell types in vitro. However, its effects on human AT and adipocyte EV secretion remain unexplored. This study aimed to investigate calpeptin's effects on EV‐mediated communication and adipocyte function, offering potential insights into therapeutic approaches for metabolic diseases. Human Simpson Golabi Behmel Syndrome (SGBS) preadipocytes were differentiated and treated with calpeptin. EVs were isolated by standard ultracentrifugation, and studied by nanoparticle tracking analysis, electron microscopy, and mass spectrometry. Diverse analyses, including RNA‐sequencing, liquid chromatography–mass spectrometry (LC–MS), and confocal microscopy were utilized to study calpeptin's effects on SGBS cells. AT samples from bariatric surgery patients were cultured ex vivo to assess calpeptin's effects on primary AT. We demonstrated for the first time that calpeptin reduces EV secretion in human SGBS adipocytes. Proteomic analyses revealed that calpeptin alters the abundances of proteins related to EV secretory pathways. While reduced EV secretion was accompanied by anti‐inflammatory effects, calpeptin also altered insulin signalling pathways and reduced adiponectin expression, suggesting negative effects on adipocyte metabolism. Indeed, LC–MS analyses of cells and EVs revealed that calpeptin altered proteins—both in cells and EVs—that are associated with stress responses. Notably, calpeptin upregulated HO‐1 in vitro and in ex vivo AT cultures, indicating induced oxidative stress in adipocytes and AT. While calpeptin shows anti‐inflammatory promise in human SGBS adipocytes, its adverse effects on insulin signalling, adiponectin expression, and signs of oxidative stress raise concerns about its therapeutic potential against obesity‐related pathologies in humans. Our results highlight the need to understand the broader impact of calpeptin on adipocyte metabolism.

Keywords: adipocytes, adipose tissue, calpeptin, extracellular vesicle inhibition, extracellular vesicles, inflammation

1. Introduction

Obesity has emerged as a pervasive global health problem, impacting approximately 13% of the worldwide adult population (Ogden et al. 2015). It is a significant risk factor for various chronic diseases, including cardiovascular disease, type 2 diabetes mellitus (T2D), metabolic dysfunction‐associated steatotic liver disease (MASLD), as well as certain malignancies. In obesity, adipocytes expand in size and number in response to overnutrition, triggering stress and inflammatory responses. Several cellular mechanisms including, e.g., increased accumulation of pro‐inflammatory macrophages, adipocyte death, hypoxia, and insulin resistance, have been shown to contribute to obesity‐induced adipose tissue (AT) inflammation (Reilly and Saltiel 2017).

In addition to various hormones and adipokines, AT secretes extracellular vesicles (EVs), key mediators of intercellular communication and regulators of biological processes. EVs are a heterogenous group of nano‐ and microsized membrane‐derived particles that carry lipids, proteins, nucleic acids, and sugars from parental to recipient cells (Yáñez‐Mó et al. 2015). Secreted by all cell types and present in all body fluids, EVs participate in both physiological and pathological processes (van Niel et al. 2018). Over recent decades, EVs have been extensively investigated for their roles in disease mechanisms and as potential diagnostic and therapeutic tools. Given their capacity to transfer cargo and modulate biological functions, pharmacological inhibition of EV formation and release is emerging as a promising therapeutic strategy, particularly in cancer (Catalano and O'Driscoll 2020).

Calpains, Ca2+‐dependent cysteine proteases involved in cytoskeletal remodelling, migration, autophagy, and apoptosis (Huttenlocher et al. 1997; Yousefi et al. 2006), are among the cellular components regulating EV release. Calpain inhibition by calpeptin has been shown to reduce EV secretion in certain cell types in vitro and, more recently, in vivo from mouse bone marrow (Gautheron et al. 2023). As AT‐derived EVs are increasingly implicated in metabolic disease, pharmacological reduction of their secretion has been proposed as a potential therapeutic approach (Rome et al. 2021). However, no studies have yet explored such compounds in AT or adipocytes.

Beyond EV regulation, calpain inhibition exerts anti‐inflammatory and anti‐fibrotic effects across various disease contexts, including pancreatic and pulmonary fibrosis (Letavernier et al. 2008; Yoshida et al. 2016). These effects are linked to reduced nuclear factor NF‐κB (NFκB) signalling and suppressed cytokine production, due to calpain‐mediated degradation of IκB, the inhibitory protein of NFκB (Lin et al. 1995; McDonald et al. 2001). A recent study demonstrated that calpain inhibition reduces inflammation and fibrosis in the AT of high‐fat diet‐fed mice and improves glucose tolerance in early obesity (Muniappan et al. 2017), suggesting therapeutic potential against obesity‐related metabolic dysfunctions. However, in vitro studies with mouse adipocytes have yielded mixed results: calpain inhibition reduced insulin‐stimulated glucose uptake in 3T3‐L1 adipocytes and in differentiating ST‐13 adipocytes, despite increased expression of peroxisome proliferator‐activated receptor γ, insulin receptor (INSR), INSR substrate 1 (IRS1), IRS2, and glucose transporter member 4 (Glut4) (Paul et al. 2003; Yajima et al. 2006). These findings highlight the complexity of calpain's role in AT metabolism and underscore the need for further investigation, particularly in human models.

In this study, we examined the effects of calpeptin on adipocyte metabolism and EV‐mediated signalling to assess its therapeutic potential in obesity‐related pathologies. Using the human Simpson Golabi Behmel Syndrome (SGBS) adipocyte cell strain, we explored calpeptin's impact on human adipocytes, an area not previously studied. We also performed liquid chromatography–mass spectrometry (LC–MS) of EVs to characterize the protein cargo of SGBS‐derived EVs for the first time and assess changes following calpeptin treatment. Finally, we treated AT samples from people with obesity to determine whether findings from SGBS cells translate to a human AT ex vivo model. To our knowledge, this is the first study to use a human adipocyte cell line to investigate calpeptin's effects, as prior research has relied on murine systems. Based on calpeptin's known anti‐inflammatory properties and previous findings in obese mouse AT, we hypothesized that (i) calpeptin would exert anti‐inflammatory effects in human SGBS adipocytes and AT ex vivo, (ii) EV secretion and proteomic profiles would be altered by calpeptin, and (iii) EV proteome changes would reflect cellular alterations.

2. Materials and Methods

2.1. Human SGBS Cell Line Culture, Differentiation, and Calpeptin Treatments

Human SGBS preadipocytes (Wabitsch et al. 2001) were cultured and differentiated as previously described (Matilainen et al. 2024) and as detailed in the Supplementary Methods. Cells were treated with calpeptin, as described in the Supplementary Methods, at concentrations ranging from 25 to 300 µM for 24 h, based on studies in which calpeptin treatment has been used to inhibit EV secretion in vitro (Catalano and O'Driscoll 2020). Confocal microscopy, RNA extraction, cDNA synthesis, real‐time quantitative PCR (RT‐qPCR), RNA‐sequencing (RNA‐seq), liquid chromatography–tandem mass spectrometry (LC‐MS/MS), SDS‐PAGE, cytokine array analysis of conditioned media (CM), Western Blotting (WB), and data analysis are also described in the Supplementary Methods. Quantitative real‐time PCR primer sequences and primary antibodies used in Western Blotting are shown in Tables S1‐S2.

2.2. Isolation of EVs from SGBS Adipocyte Medium

EVs from CM from SGBS adipocyte treatments were obtained and stored until further analysis as previously described (Matilainen et al. 2024). The size distribution and concentration of particles by nanoparticle tracking analysis (NTA), as well as the transmission electron microscopy (TEM) analysis of the EV preparations were performed as previously described (Matilainen et al. 2024).

2.3. Patient Samples from Bariatric Surgeries for AT and Blood Collection

A total of 6 subjects with obesity (all women) undergoing Roux‐en‐Y gastric bypass operation and participating in the ongoing Kuopio OBesity Surgery study (Pihlajamäki et al. 2012) were included for obtaining AT samples (Table 1). Anthropometric, clinical, and biochemical parameters were assessed at baseline, as previously described (Vaittinen et al. 2016; Walle et al. 2016). Blood samples were collected at Kuopio University Hospital in Kuopio, Finland, following a 12‐h o/n fast prior to surgery. The study protocol was approved by the Ethics Committee of Northern Savo Hospital District (54/2005, 104/2008, 27/2010, and 1108/2018), and carried out in accordance with the Declaration of Helsinki. Written informed consent was obtained from all participants.

TABLE 1.

Clinical characteristics of the patients from whom fasting plasma and adipose tissue samples were obtained.

Gender (n; male/female) 0/6
Age (years) 49 ± 4
T2D (n) 3/6
Individuals on antihyperlipidemic medication (n) 1/6
Body mass index (kg/m2) 40.9 ± 1.72
Triacylglycerols (mmol/l) 1.3 ± 0.23
Total cholesterol (mmol/l) 3.5 ± 0.40
HDL‐cholesterol (mmol/l) 1.2 ± 0.06
LDL‐cholesterol (mmol/l) 1.7 ± 0.42
Glucose (mmol/l) 5.7 ± 0.13
Open in a new tab

Note: Mean ± SEM.

Abbreviations: HDL = high‐density lipoprotein, LDL = low‐density lipoprotein, T2D = type 2 diabetes.

2.4. AT Collection, Processing, Culturing, and Calpeptin Treatments

Visceral AT (VAT; omental) and subcutaneous AT (SAT; abdominal) ex vivo cultures and their calpeptin treatments were performed as described in the Supplementary Methods.

2.5. Statistical Testing

Different statistical analyses were performed using the IBM SPSS v21.0 software (IBM, Armonk, NY, USA). Comparisons between EV counts secreted by SGBS cells were performed with the generalized linear model. Otherwise, comparisons with at least three treatment or sample groups were performed with the Kruskal–Wallis nonparametric analysis of variance (ANOVA) followed by the Dunn's post hoc test. Nonparametric tests between two sample groups were performed with the Mann–Whitney U test. Associations between variables were assessed using Spearman's rank correlation coefficients. Correlation analyses were performed using two‐tailed tests. The Pvalue <0.05 was considered statistically significant. The results are presented as the mean ± SEM.

3. Results

3.1. Validation Studies of SGBS EVs

For the experiments, human preadipocyte SGBS cells (Figure 1a) were first differentiated into adipocytes (Figure 1b), after which EVs were isolated from the CM by standard ultracentrifugation. For validating the purity and quality of EV isolates, EVs were first fluorescently labelled against CD63 and CD9 tetraspanins and imaged with high resolution confocal microscopy (Figure 1c). The presence of EV‐markers was further confirmed by WB, revealing CD9, CD63, tumor susceptibility 101 (TSG101), β‐actin, and programmed cell death 6 interacting protein (Alix) in EVs from differentiated SGBS cells (Figure 1d). Adiponectin, an adipocyte‐derived protein previously shown to act as an adipocyte‐EV marker (Connolly et al. 2015; Matilainen et al. 2024), was also present. Cross‐contamination of EV isolates with cell organelles was excluded by the absence of calnexin (Figure 1d). TEM analyses revealed the occurrence of EVs with typical cup‐shaped morphology, together with sufficient sample purity (Figure 1e). As an additional quality metric, a subset of EV preparations was analysed for total protein concentration and particle concentration by NTA (Figure S1). NTA‐derived particle concentrations correlated with EV preparation protein concentrations across the analysed samples (R2 = 0.9739, P = 0.0018), and the particle‐to‐protein ratios ranged from 1.35 × 108 to 1.87 × 108 particles/µg protein.

FIGURE 1.

FIGURE 1

Open in a new tab

Characterization of extracellular vesicles (EVs) from Simpson Golabi Behmel Syndrome (SGBS) cells. (a) The differentiation of pre‐SGBS cells (b) into mature, lipid‐laden SGBS cells was induced, after which EVs were isolated from culture medium by differential steps of ultracentrifugation. (c) The presence of CD63 and CD9 EV‐markers was confirmed by fluorescent labelling and confocal microscopy of EV samples. (d) The presence of adipocyte‐derived material (adiponectin), tumor susceptibility 101 (TSG101), CD63, CD9, programmed cell death 6 interacting protein (Alix), and β‐actin, as well as the absence of calnexin were further analysed by Western Blotting from EV samples. (e) Transmission electron micrograph showing several particles with typical EV morphology, and the high purity of samples.

3.2. Calpeptin Reduces EV Secretion from Human Adipocytes

To assess whether calpeptin affects calpain expression and EV secretion in human adipocytes, differentiated SGBS cells were treated with varying calpeptin concentrations for 24 h. Cell viability was confirmed via propidium iodide staining and confocal microscopy (Figure S2), showing no significant apoptosis. WB revealed that calpain‐2 was more abundantly expressed than calpain‐1, and both proteins were downregulated by calpeptin (Figure 2a,b). EV secretion was then analysed with NTA, showing a decrease at 25 and 50 µM, but an increase at 150 and 300 µM (Figure 2c). Based on these findings, lower concentrations were selected for further experiments. Despite reduced EV secretion, calpeptin did not significantly alter EV size distribution at 25 or 50 µM (Figure 2d).

FIGURE 2.

FIGURE 2

Open in a new tab

Treatment of mature Simpson Golabi Behmel Syndrome (SGBS) cells with calpeptin and its effect on extracellular vesicle (EV) secretion. Mature SGBS cells were treated with differing concentrations of calpeptin for 24 h, after which the effects on (a) calpain‐2 and (b) calpain‐1 protein levels were studied by Western Blotting. Band densities were normalized to β‐actin, and results were calculated from two independent experiments. (c) Changes in EV secretion were analysed by nanoparticle tracking analysis (NTA). Results are presented as mean particle count ± SEM secreted per cell, from four independent experiments in case of treatments with 25 and 50 µM calpeptin, and two in case of 150 µM and 300 µM treatments. Generalized linear model (GLM) ctrl vs. calpeptin 25 µM and 50 µM *P = 0.002. (d) Size distribution of secreted particles in 25 and 50 µM treatments was also obtained by NTA.

3.3. Calpeptin Induces Anti‐Inflammatory Effects on Adipocytes

Given the reported anti‐inflammatory effects of calpain inhibition via reduced NFκB signalling and cytokine production (Letavernier et al. 2008; Tabata et al. 2010), we examined whether calpeptin exerts similar effects in adipocytes. RT‐qPCR analysis showed that 50 µM calpeptin suppressed mRNA levels of CCL2, CXCL8, IL1B, IL6, TNFRSF1A, and NFκB1 (Figure 3a), prompting its use in subsequent experiments. Cytokine array of CM (Supplementary Methods) confirmed reduced secretion of MCP‐1, IL‐8, and IL‐1β, consistent with mRNA data (Figure 3b). Additionally, levels of acute phase protein (C‐reactive protein) and MCP‐3 were lowered, along with several adipokines linked to AT inflammation (Visser et al. 1999; Ouchi et al. 2011; Hagberg and Spalding 2024): leptin, osteopontin, retinol‐binding protein 4 (RBP4), and resistin. Other inflammatory mediators, including ILs, chemokines, and acute‐phase proteins, were also decreased (Figure 3b). Notably, reductions in platelet‐derived growth factor subunit A (PDGF‐A), PDGF‐B, and cripto‐1 suggested anti‐fibrotic effects. Together, these results indicate that calpeptin alleviates inflammation in adipocytes at both transcript and protein levels.

FIGURE 3.

FIGURE 3

Open in a new tab

(a) The mRNA expression levels of CCL2, CXCL8, IL1B, IL6, TNFRSF1A, and NFκB1 in Simpson Golabi Behmel Syndrome cells treated with 50 µM calpeptin were analysed by RT‐qPCR. The data were expressed as fold changes compared to control after normalization to HPRT1. IL6 and CXCL8 results were analysed from three, and TNFRSF1A and CCL2 from four independent experiments, respectively. Data are presented as mean ± SEM. *P = 0.014 for CCL2 and TNFRSF1A, and 0.037 for CXCL8 and IL6, respectively (Mann–Whitney U test). (b) Inflammatory cytokines analysed from conditioned media (CM) of calpeptin‐treated cells by cytokine array. Data are presented as mean pixel intensities, obtained from duplicate analyte spots on each array membrane (calpeptin control, calpeptin 50 µM). Relative changes in analyte levels were obtained by normalizing the analyte pixel intensities in calpeptin 50 µM CM membrane to control CM membrane spot pixel intensities. Results are obtained from CM from one experiment. BAFF: angiogenin, ENA‐78: CXCL5, GROα: growth‐regulated α protein, IL‐1β: interleukin‐1β, IL‐12 p70: interleukin‐12 subunit β, I‐TAC: CXCL11, MIG: monokine induced by interferon‐γ (CXCL9), MIP1α: macrophage inflammatory protein 1‐α (CCL3), MIP‐1β: macrophage inflammatory protein 1‐β (CCL4), MIP‐3α: macrophage inflammatory protein 3 α (CCL20), MIP3‐β: macrophage inflammatory protein 3 β (CCL19), PDGF‐AA: platelet‐derived growth factor subunit A, PDGF‐BB: platelet‐derived growth factor subunit B, PLF4: platelet factor 4, RAGE: advanced glycosylation end product‐specific receptor, RBP4: retinol‐binding protein 4, SDF‐1α: stromal cell‐derived factor 1. (c) The heatmap showing differentially expressed genes (DEGs, adj. P value <0.05) between control and calpeptin‐treated cells, obtained from RNA‐seq data from three experiments. (d) DEG GO enrichment analysis of biological processes (adj. P value <0.05) related to downregulated DEGs.

To further explore calpeptin's effects, RNA‐seq was performed on treated SGBS adipocytes, identifying 75 differentially expressed genes (DEGs) (59 were downregulated, 16 upregulated; Figure 3c, Table S3). IL1B and other genes involved in acute and innate immune responses (SAA1, SAA2, LBP, C6, ORM1, and HP) were suppressed. ADIPOQ was downregulated, while HMOX1 was upregulated. Gene ontology (GO) analysis linked downregulated DEGs to pathways related to inflammation, IL‐1 and tumor necrosis factor production, glucose metabolism and transport, xenobiotic responses, and other metabolic processes (Figure 3d, Table S3).

3.4. Calpeptin Disrupts Adiponectin Signalling and Upregulates HO‐1 in Adipocytes

To further examine calpeptin's impact on adipocyte metabolism, we performed LC−MS proteomic analysis of treated SGBS cells. Initial Student's t‐test analysis of our LC–MS data (p‐value <0.05) identified 184 proteins with altered expression (Table S4). After excluding isoforms and unreviewed trEMBL entries at UniProt, 92 proteins remained. Subsequent nonparametric testing revealed additional 21 differentially expressed proteins, yielding 113 proteins in total (Table S5, Figure 4a). Two of these proteins, tyrosine‐protein kinase ABL1 and tubulin alpha‐1A, shared peptides with another protein of the same protein family. Thus, as their exact identification could not be performed, they were combined into a single entry: tyrosine‐protein kinase ABL1/tyrosine‐protein kinase ABL2, and tubulin alpha‐1A chain/tubulin alpha‐1C chain (Table S5). We subsequently identified enriched GO terms associated with the biological processes in which all differentially expressed proteins are involved (Table S6). The obtained terms were analysed with the REViGO tool to group similar terms (Table S7). The main GO terms obtained with REViGO are displayed in the dotplot (Figure 4b). These terms included various inflammation‐related processes, but also response to xenobiotic stimulus, external stimulus, and glucose, as well as hydrogen peroxide‐related processes.

FIGURE 4.

FIGURE 4

Open in a new tab

(a) Volcano plot of the differentially expressed proteins between control and calpeptin‐treated Simpson Golabi Behmel Syndrome cells, obtained from mass spectrometry (LC–MS) data. The X‐axis shows the log2 fold change (treated cells to control cells), and the Y‐axis shows the –log10 P‐values. Upregulated and downregulated proteins are indicated in red and blue, respectively. Proteins marked with * indicate significant changes (P‐value <0.05) based on a nonparametric test, while others are significant based on a Student's t‐test. Results were obtained from five independent experiments. (b) Dot plot showing enriched Gene ontology (GO) terms associated biological processes in differentially expressed proteins in calpeptin‐treated cells. The Y‐axis shows the log(P‐values). Dot sizes represent the number of proteins associated with each term.

Despite solid evidence of reduced inflammation, adiponectin was notably downregulated (0.53‐fold, Figure 5a), consistent with RNA‐seq data showing suppressed ADIPOQ expression. This was validated by RT‐qPCR and WB (Figure 5b,c), and cytokine array analysis revealed reduced adiponectin levels in CM (Figure 5d). These findings suggest that calpeptin may impair adipocyte metabolic function. Additionally, both RNA‐seq and LC–MS showed the increased expression of heme oxygenase 1 (HO‐1, encoded by HMOX1; Figure 5e) and reduced haptoglobin, encoded by HP gene (Figure 5f). Investigations of human AT and adipocytes have revealed that upregulated HO‐1 is associated with insulin resistance, inflammation, and oxidative stress (Jais et al. 2014; Moreno‐Navarrete et al. 2017).

FIGURE 5.

FIGURE 5

Open in a new tab

Differentiated Simpson Golabi Behmel Syndrome cells were treated with 50 µM calpeptin for 24 h, after which cells were harvested for RT‐qPCR and mass spectrometry (LC–MS) analyses. (a) Adiponectin protein expression levels were studied by LC−MS. Data were acquired from five independent experiments. (b) The expression levels of ADIPOQ gene were studied by RT‐qPCR. The data were expressed as fold changes compared to control after normalization to HPRT1. Results of four independent experiments are shown. Data are presented as mean ± SEM. **P = 0.005 (Mann–Whitney U test). (c) Protein expression levels of adiponectin were further validated by Western Blotting from cell lysates. Adiponectin band densities, all normalized to β‐actin, were calculated from four independent experiments. P = 0.014 (Mann–Whitney U test). (d) Adiponectin levels in conditioned media were analysed by cytokine array analysis. Data are presented as mean pixel intensities, obtained from duplicate analyte spots on each array membrane (calpeptin control, calpeptin 50 µM). Results are obtained from conditioned media (CM) from one experiment. (e, f) Protein levels of heme oxygenase 1 (HO‐1) and haptoglobin were obtained from LC−MS, from five independent experiments. Data are presented as mean ± SEM, P = 0.016 (Mann–Whitney U test).

3.5. Calpeptin Impairs Insulin Signalling and Alters Glut4 Localization

Disrupted adiponectin signalling has been linked to T2D development and is associated with inflammation‐driven insulin resistance and defects in Glut4 protein translocation from the cytosol to the plasma membrane for glucose uptake (Hotta et al. 2000; van Gerwen et al. 2023). Consistent with this, the expression of SLC2A4, coding Glut4, was suppressed in calpeptin‐treated cells in our RNA‐seq data. Thus, to further investigate if calpeptin would alter insulin signalling and Glut4 function, we examined the phosphorylation status of INSR and its downstream components in calpeptin‐treated SGBS adipocytes (Figure 6a). Interestingly, according to this array, the phosphorylation of INSR, IRS1 ser318, and insulin‐like growth factor 1 receptor (IGF1R) decreased, while the phospho‐SHC (Y427) levels remained unchanged.

FIGURE 6.

FIGURE 6

Open in a new tab

Effects of calpeptin treatment on insulin‐related glucose uptake in Simpson Golabi Behmel Syndrome (SGBS) cells. (a) Mature SGBS cells were treated with 50 µM calpeptin for 24 h, after which the cells were lysed for the insulin receptor phosphorylation array analysis. Results were acquired from quantified spot intensities, normalized to control spots, and presented as mean fold‐change (from one experiment). (b) Protein expression levels of p‐IRS1 S307, p‐PI3K p55, and Akt were analysed by Western Blotting from cell lysates. Band densities, all normalized to β‐actin, were calculated from two, three, and four independent experiments, respectively. Data are presented as mean ± SEM. *P‐values for p‐PI3K p55 (Tyr199) and Akt 0.037 and 0.014 (Mann–Whitney U test), respectively. (c) Glut4 subcellular localization in vehicle‐ and calpeptin‐treated cells. Cells were treated with vehicle or calpeptin, and Glut4 localization was studied by immunofluorescent staining of Glut4 and confocal microscopy. Cells were first treated with 50 µM of calpeptin or control vehicle for 24 h, followed by 4‐h insulin starvation and 20‐min 100 nM insulin stimulation. Cells were thereafter fixed, and Glut4, lipid droplets (LipidSpot), and nuclei (DAPI) were stained for confocal microscopy. Images are maximum intensity projections from Z‐stacks, representative of both treatments. In vehicle‐treated cells, Glut4 signal was detected at the plasma membrane as well as diffusely throughout the cytoplasm, whereas in calpeptin‐treated cells Glut4 signal appeared predominantly as cytoplasmic, concentrated structures.

To further validate the results suggesting insulin resistance via impaired INSR signalling, which further mediates PI3K/Akt and Glut4 transportation, we measured the protein ser307 phosphorylation levels of IRS1, p‐PI3K, and total Akt levels by WB (Figure 6b). Increased trend of ser307 p‐IRS1 levels indicated negative effects on insulin signalling, and decreased p‐PI3K and total Akt levels downregulated PI3K/Akt pathway. To confirm the ability of calpeptin to disrupt Glut4 trafficking to the plasma membrane, we performed 4‐h insulin starvation and subsequent 20‐min 100 nM insulin stimulation after calpeptin treatments of cells. Immunofluorescence staining of Glut4 was performed, and cells were analysed with confocal microscope (Figure 6c). In vehicle‐treated cells, Glut4 signal was localized either on the plasma membrane or evenly in the cytoplasm. However, in calpeptin‐treated cells Glut4 mainly existed as somewhat concentrated aggregates in the cytoplasm, indicating altered translocation to the plasma membrane.

3.6. Proteomic Profile of SGBS EVs

We performed a comprehensive LC–MS proteome analysis of SGBS EVs, which, to our best knowledge, has not been previously reported. A total of 4906 peptides were identified (Table S8), corresponding to 1579 proteins with at least one unique peptide, excluding isoforms, duplicates, and trEMBL entries (File S1). Eight proteins shared peptides with protein family members, preventing their distinct identification: β‐actin/γ‐actin, α‐cardiac actin/α‐actin‐1, myosin‐9/myosin‐10, and caveolin‐1/‐3. These proteins were combined into a single entry in the original dataset (Table S8). However, due to the critical relevance of these proteins as listed in the Vesiclepedia database, the first proteins of the pairs were considered as identified proteins: β‐actin, α‐cardiac actin, myosin‐9, and caveolin‐1. Thus, 1585 proteins were used for downstream analyses.

Consistent with WB validation, common EV markers CD63, Alix, and ACTB/ACTG (β‐actin shared peptides with γ‐actin) were detected, along with adipocyte‐specific EV marker adiponectin (Figure 7a). Although TSG101 and CD9 were identified by WB, they were not detected by LC–MS in EVs or cells. In addition to adiponectin, the EV proteome included adipocyte metabolism‐related proteins such as leptin, fatty acid‐binding protein 4 (FABP4), fatty acid synthase (FASN), acetyl‐coenzyme A carboxylase (ACACA), ACACB, and perilipin‐1, as well as inflammation‐related proteins, such as IL‐6, complement C3, and HP. Functional enrichment analysis of the full SGBS EV proteome revealed GO terms related to extracellular exosome, collagen‐containing extracellular matrix, focal adhesion, endoplasmic reticulum (ER), and cytosol (Figure 7b, File S1). The tissue expression analysis highlighted AT, soft tissue types, hair, skin, and ovary (Figure 7c, File S1). Comparison with the Vesiclepedia EV database showed a substantial overlap: 1057 Vesiclepedia proteins were matched, including 75 of the top 100 EV markers (Figure 7d).

FIGURE 7.

FIGURE 7

Open in a new tab

(a) Examples of proteins found from Simpson Golabi Behmel Syndrome (SGBS) cell‐derived extracellular vesicles (EVs). (b) Gene Ontology (GO) terms of Cellular components and (c) Tissue expression in SGBS EV proteome. (d) Venn diagram showing the overall number of SGBS EV proteins found from the Vesiclepedia database, and from the list of the 100 most commonly identified EV proteins in the Vesiclepedia. (e) Venn diagram showing proteins found from SGBS EVs and cells. (f) Enriched disease terms obtained from DISGNET with DAVID, from proteins found from SGBS EVs but not from cells. (g) Comparison of the SGBS EV proteome to previously published human visceral (VAT) and subcutaneous adipose tissue (SAT) EV proteomes (Camino et al. 2022), as well as mouse 3T3‐L1 (Durcin et al. 2017) and 3T3‐F442A (Lazar et al. 2016) EV proteomes. (h) Comparison of SGBS EV proteins to human AT proteome (Human Protein Atlas) and previously reported human AT VAT and SAT EV proteins.

Of the identified proteins, 889 were also present in SGBS cells (Figure 7e), including those involved in glycolysis (e.g., α‐enolase, phosphoglycerate kinase 1, fructose‐bisphosphate aldolase A, triosephosphate isomerase, glucose‐6‐phosphate isomerase, glyceraldehyde‐3‐phosphate dehydrogenase, pyruvate kinase 2/3, and L‐lactate dehydrogenase A) and fatty acid biosynthesis (ACACA, ACACB, FASN, long‐chain‐fatty‐acid‐coenzyme A ligase 1, 5–6) (File S1). Notably, 696 proteins were exclusive to EVs, including those linked to complement and coagulation cascades (e.g., plasminogen activator inhibitor 1 (PAI‐1), complement C5, C9, fibrinogen α) and matrix organization (e.g., matrix metalloproteinase‐2 (MMP‐2), MMP‐11, MMP‐13, bone morphogenetic protein 1, and various collagen proteins). Functional enrichment using DISGENET terms revealed associations with liver cirrhosis, other liver pathologies, and breast cancer (Figure 7f, File S1).

Finally, we compared SGBS EV proteome to the human AT proteome (Human Protein Atlas), previously reported human VAT and SAT EVs (Camino et al. 2020), and mouse adipocyte 3T3‐L1 (Durcin et al. 2017) and 3T3‐F442A (Lazar et al. 2016) EVs. SGBS EVs shared more proteins with human VAT EVs (243 proteins) than SAT EVs (195 proteins) (Figure 7g, File S1). Overall, the SGBS EV proteome more closely resembled the human AT EVs than mouse‐derived 3T3‐L1 EVs, sharing 126 proteins with 3T3‐L1 and 174 with 3T3‐F442A EVs. Remarkably, 1233 proteins in SGBS EVs were also present in the human AT proteome, compared to 492 and 331 proteins previously identified in VAT and SAT EVs, respectively (Figure 7h, File S1).

3.7. Calpeptin Reduces CD63 and Epoxide Hydrolase 1 Protein Levels in EVs

Proteomic analysis of EVs allowed us to assess calpeptin‐induced changes in EV protein cargo. Initial Student's t‐test analysis of the data (P‐value <0.05) identified 11 proteins with altered abundance in EVs from calpeptin‐treated cells (calp EVs) compared to control EVs (ctrl EVs): keratin type II cytoskeletal 5 (KRT5), keratin type II cuticular Hb5 (KRT85), NEDD4 family‐interacting protein 2 (NFIP2), SNF‐related serine/threonine‐protein kinase, reticulon‐1, 3, 4, TOX high mobility group box family member 3, lipopolysaccharide‐binding protein (LBP), PRA1 family protein 3 (PRAF3), and microtubule‐actin cross‐linking factor 1 (MACF1) (Table S8). Additionally, 14‐3‐3 protein zeta/delta (1433Z) showed a decreasing trend (fold‐change 0.78, p‐value 0.0567). Mann–Whitney U test revealed 8 more differentially expressed proteins: lactadherin, insulin‐like growth factor‐binding protein 3 (IBP3), mitochondrial metalloendopeptidase (OMA1), caveolin‐1/3 (CAV1/3), epoxide hydrolase 1 (HYEP), transforming growth factor‐beta induced protein ig‐h3 and, notably, CD63 (Figure 8a, Table S9). After excluding trEMBL entries and isoforms, 19 proteins were considered differentially expressed (Table S9). Interestingly, IBP3 was reduced in EVs (0.47‐fold‐change), despite its increase in CM upon calpeptin treatment.

FIGURE 8.

FIGURE 8

Open in a new tab

(a) Proteins with differing abundance in extracellular vesicles (EVs) under 50 µM calpeptin treatment. Results were obtained from five independent experiments, and data are presented as mean + SEM. All changes displayed were statistically significant (P‐value <0.05 with Student's t‐test or Mann–Whitney U test. (b) Altered proteins and their key associated biological processes and cellular components illustrated in a Sankey diagram. (c) Protein–Protein Interaction network analysis of some proteins that were altered in EVs and cells upon calpeptin treatment, based on the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) with medium confidence interactions (0.400). Colour and pattern representation of proteins defines the regulation and localization. The colour of the circles defines the direction of protein regulation: red indicates upregulation, while blue indicates downregulation. The pattern within the circles illustrates the context of the response: vertical stripes indicate the response that has occurred in cells, and diagonal stripes indicate the response in EVs. Colour lining connecting proteins defines the source interaction – Grey: co‐expression, yellow: textmining, purple: experimentally determined, turquoise: from curated databases, dark blue: gene co‐occurrence, and light blue: protein homology.

To explore the biological relevance of these changes, we manually annotated associated cellular components and processes (Figure 8b). Several proteins, such as CD63, LBP, KRT5, KRT85, 1433Z, lactadherin, IBP3, and reticulon‐3 (RTN3), were linked to extracellular exosomes and space. Others, including RTN1, NFIP2, CAV1, CAV3, PRAF3, MACF1, and OMA1, were associated with membranes, such as plasma, ER, mitochondrial, Golgi, and multivesicular bodies. Biological processes included integrin signalling, intermediate filaments, keratinization, vacuolar transport, glucose metabolism, and responses to hypoxia, oxidative stress, starvation, xenobiotics, and inflammation.

To identify potential links between EV and cellular proteins altered by calpeptin, we conducted the Protein–Protein Interaction network analysis of altered proteins in EVs and cells (Figure 8c). This revealed a cluster of proteins affected in both EVs and cells, with HO‐1 and adiponectin at the centre, both consistently altered in RNA‐seq and LC−MS data. HO‐1 interacted with thioredoxin‐2 (TXN2), IL‐1β, HYEP, and HP via “co‐expression” and “textmining” evidence. TXN2 showed a near‐significant reduction in cells (fold‐change 0.54, p‐value 0.076), HP was suppressed at protein level, and IL‐1β was downregulated at mRNA level. HYEP, reduced in calp EVs, was also linked to HO‐1. IL‐1β further connected to IBP3, 1433Z, LBP, and CD63, which were all reduced in calp EVs but unchanged in cells. These findings suggest that calpeptin alters a protein cluster spanning both cells and EVs, potentially contributing to adverse metabolic effects and impaired EV secretion in adipocytes.

3.8. Calpeptin Upregulates HMOX1 in Patient SAT Cultured Ex Vivo

Finally, to determine whether calpeptin would similarly target HO‐1, TXN2, and HP in patient VAT and SAT ex vivo cultures, the mRNA expression of these genes was evaluated by RT‐qPCR. In VAT cultures, trends of suppressed expression of TXN2 and HP were observed, but these changes did not reach statistical significance (Figure 9a). In SAT, on the other hand, HP expression had a decreasing trend, while the upregulation of HMOX1 was statistically significant following treatment with 50 µM calpeptin (Figure 9b). Interestingly, although TXN2 and HP were not clearly changed in calpeptin‐treated AT cultures, TXN2 expression negatively correlated with body mass index (BMI) in SAT samples treated with 25 µM of calpeptin (Figure 9c). In VAT cultures, HP expression was also associated with BMI after 25 µM treatment, showing a similar negative correlation (Figure 9d). To conclude, although HMOX1 only showed a significant upregulation in SAT cultures, the trends observed in TXN2 and HP suggest that calpeptin may have subtle effects on these genes. Moreover, the correlations between TXN2 and HP with BMI suggest that calpeptin has differential effects based on patient adiposity.

FIGURE 9.

FIGURE 9

Open in a new tab

Treatment of ex vivo subcutaneous (SAT) and visceral adipose tissues (VAT) with calpeptin. (a) After 24‐h incubation with 25 or 50 µM of calpeptin, VAT and (b) SAT samples were collected for RT‐qPCR to study the mRNA levels of selected genes. The data were expressed as fold changes compared to control after normalization to GAPDH. The samples from 5 subjects were analysed, presented as mean ± SEM. *P = 0.011 (Kruskal–Wallis nonparametric analysis of variance). (c) Correlation between body mass index (BMI) and the expression of TXN2 in SAT treated with 25 µM of calpeptin. (d) Correlation between BMI and the expression of HP in VAT treated with 25 µM of calpeptin. Spearman's rank correlation coefficient –0.900, *P ‐value 0.037. AT samples from 5 subjects with obesity were analysed in total.

4. Discussion

AT EVs have been increasingly recognized for their role in the development of T2D, MASLD, and cardiovascular disease, raising the interest in these EVs as a therapeutic strategy against obesity‐related conditions (Rome et al. 2021). Pharmacologically active compounds that inhibit EV secretion include calpeptin, which targets calpains and has been previously used to inhibit EV formation in multiple cell types (Catalano and O'Driscoll 2020) but, to our best knowledge, not in adipocytes. Interestingly, a recent in vivo mouse study demonstrated that calpain inhibition represses obesity‐induced inflammatory and fibrotic responses in AT (Muniappan et al. 2017), but the effects of calpain inhibition on human AT metabolism remain unexplored. To address this knowledge gap, we examined the impact of calpeptin on human SGBS adipocytes with a special focus on EV secretion and protein cargo, as well as on adipocyte responses, to see if calpeptin would have therapeutic potential in obesity‐related disorders. We demonstrated, as hypothesized, that calpeptin reduced EV secretion in human adipocytes, and exerted anti‐inflammatory effects. However, despite its anti‐inflammatory actions, calpeptin clearly disrupted insulin signalling pathways and led to a marked downregulation of adiponectin, indicating that there would also be adverse effects on adipocyte metabolism. In addition, our LC–MS analyses revealed that calpeptin induced changes in both cellular and EV proteins associated with various stress responses, including oxidative stress. Notably, the increased expression of HO‐1, in both in vitro SGBS adipocytes and patient SAT ex vivo cultures, suggests that calpeptin could trigger oxidative stress pathways, which have been linked to insulin resistance and inflammation in human AT.

Over the last decade, proteomic profiles of adipocyte and AT EVs have raised great interest. Previous detailed LC–MS investigations have focused on EVs from mouse adipocyte 3T3‐L1 and 3T3‐F442A cell lines (Durcin et al. 2017; Lazar et al. 2016), primary rat adipocytes (Lee et al. 2015), rat inguinal fat pad extracts (Zhang et al. 2020) and, more recently, on EVs from human VAT and SAT, obtained from people with obesity (Camino et al. 2022). While mouse and rat adipocyte EV material has been widely studied, it offers only a limited understanding of the adipocyte secretome in humans due to well‐documented species differences in adipokine secretion (Wang et al. 2008). The secretome of human SGBS adipocytes has previously been shown to closely resemble that of human AT (Rosenow et al. 2010), but the protein profile of SGBS EVs remained unexplored. Kranendonk et al. (Kranendonk et al. 2014) addressed this area with WB and multiplex immunoassays, but a detailed LC–MS analysis was absent. Thus, we conducted the first comprehensive proteomic analysis of SGBS EVs using LC–MS. As expected, we demonstrated that SGBS EV proteomes more closely resemble human VAT and SAT EV proteomes than those of 3T3‐L1 and 3T3‐F442A EVs. Consistent with Kranendonk et al., we identified CD63, adiponectin, leptin, FABP4, IL‐6, and PAI‐1 in SGBS EVs. However, proteins, such as flotillin‐1, CXCL10, TNFα, IL‐8, macrophage colony‐stimulating factor, macrophage migration inhibitory factor, resistin, thrombopoietin, and RBP4, were not detected, likely due to limitations in our LC–MS methodology, such as the lack of membrane enrichment strategies (Schey et al. 2013). This also presumably explains why CD9 and TSG101 were only detected by WB in our investigations.

Interestingly, we observed a high number of proteins in SGBS EVs that were not detected in the cells themselves. This could be due to matrix effects in the LC–MS analysis (Trufelli et al. 2011) or because these proteins are more abundant in EVs, suggesting their important roles in adipocyte communication, transport, and signalling. Functional enrichment analysis of these proteins revealed associations with liver diseases and breast cancer, pathologies in which AT EVs have recently been implicated (Lazar et al. 2018; Bond et al. 2022). A noteworthy finding is the remarkably higher number of proteins detected in SGBS EVs (1584) compared to previous studies on human VAT and SAT EVs, which identified 575 and 403 proteins, respectively (Camino et al. 2022). Additionally, the majority of SGBS EV proteins (77.7%) were shown to be present in the human AT proteome, according to the Human Protein Atlas database. This indicates that SGBS EVs offer a valuable tool for modelling the human AT secretome.

To our best knowledge, this is the first report showing that calpeptin reduces EV secretion in human SGBS adipocytes. Previous studies have demonstrated calpeptin's ability to inhibit EV release in few other cell types, including platelets, human embryonic kidney cells (HEK293), prostate cancer cells (PC3) (Catalano and O'Driscoll 2020), and THP‐1 monocytes (Grant et al. 2011). However, as noted by Catalano and O'Driscoll (2020), many of these studies lacked assessment of cellular effects. Thus, further investigation into calpeptin's capacity to regulate EV release was warranted, emphasizing comprehensive EV characterization, dose‐dependent effects, and potential cellular toxicity. Because calpeptin acts pharmacologically, these findings should be followed by genetic or isoform‐selective approaches to confirm calpain dependency and to distinguish calpain‐mediated effects from potential compound‐specific responses.

A recent investigation by McNamee et al. (McNamee et al. 2023) is among the few to evaluate calpeptin's effects on EV secretion across different concentrations, using EV particle counts to assess inhibition without inducing toxicity. They found that 20 µM calpeptin reduced EV secretion by 88% in an aggressive breast cancer cell line, with residual EVs showing minimal impact on cell migration. In contrast, our data show that calpeptin reduces EV secretion in SGBS cells by 30% at a concentration of 25 µM and by 26% at 50 µM. Although these reductions in EV secretion were modest, calpeptin also exhibited anti‐inflammatory effects at these concentrations, consistent with prior findings. Notably, higher concentrations promoted EV secretion and triggered inflammatory responses, highlighting the need to monitor multiple cellular effects when optimizing calpeptin dosing for EV inhibition in vitro.

Because calpeptin has been proposed to affect microvesicle secretion, we examined whether treatment altered the overall particle size distribution measured by NTA. No major shift was observed, although larger particles were rare in our preparations. Thus, additional subtype‐sensitive approaches, such as fluorescence‐based NTA, would be needed to determine whether calpeptin selectively affects specific EV subtypes. In contrast, EV LC–MS analysis provided more detailed insights into calpeptin‐associated changes in EV cargo and putative secretory pathways. Our results show that calpeptin reduced the abundance of several membrane‐, exosome‐, and extracellular space‐associated proteins. For instance, NFIP2, a transmembrane protein present in Golgi and multivesicular bodies that has been suggested to act in protein trafficking (Shearwin‐Whyatt et al. 2004), was significantly reduced in calp EVs vs. ctrl EVs. Moreover, the abundances of CD63 and CAV1/3 also decreased in EVs. CD63 is highly enriched in multivesicular bodies and plasma membrane, which plays a role in the secretion of EVs with endosomal origin (Hurwitz et al. 2016). Also, CAV1, a component of lipid rafts that is often present in EVs, has an emerging role as a regulator of EV release (Ni et al. 2020). Reduced EV secretion by calpeptin, accompanied by a lower abundance of membrane‐ and EV‐associated proteins in the remaining EVs, is a noteworthy finding that warrants further investigation in the context of pharmacological EV inhibition. While these observations suggest that calpeptin alters the molecular composition of SGBS adipocyte‐derived EVs, the functional consequences of these changes for recipient cells were not assessed in the present study and will require dedicated EV‐based assays in relevant target cell models. Our results offer new directions for studying the calpain targets and their functional roles in EV biogenesis.

The reduction in EV secretion from SGBS cells was accompanied by broad anti‐inflammatory effects, evidenced by decreased expression of CCL2, CXCL8, and IL1B, and reduced secretion of MCP‐1, IL‐1β, and IL‐8. These findings are consistent with previous reports of calpeptin's anti‐inflammatory and anti‐fibrotic properties in various cell types and in vivo models of pathologies, including SARS‐CoV‐2 infection (Reinke et al. 2023), pancreatic cancer (Yoshida et al. 2016), pulmonary inflammation (Zuo et al. 2018), and acute kidney injury (Wu et al. 2022). Notably, calpain inhibition has also been shown to suppress inflammation and fibrosis in the AT of high‐fat diet mice (Muniappan et al. 2017). Given calpain's role in EV biogenesis, it is plausible that reduced EV shedding would have contributed to the alleviated inflammation observed in mouse AT. We have previously demonstrated that adipocyte EVs respond rapidly to inflammatory stimuli in human obesity and that increased EV secretion is detectable in circulation (Matilainen et al. 2024). In light of the growing role of EVs in AT inflammation, our findings suggest that calpeptin may offer a strategy to mitigate AT‐related inflammation.

However, our results suggest that calpeptin also impaired insulin signalling in SGBS adipocytes. We observed reduced SLC2A4 and IGF1 expression, together with decreased phosphorylation of INSR and IGF1, and altered IRS1 serine 307 as well as 318 phosphorylation, collectively consistent with the attenuation of PI3K/Akt signalling (Pei et al. 2022). This interpretation is further supported by altered insulin‐stimulated Glut4 localization. These results are in line with previous in vitro studies showing that calpain inhibition suppresses insulin‐stimulated glucose uptake in murine primary adipocytes (Sreenan et al. 2001) and ST‐13 and 3T3‐L1 adipocyte cell lines (Paul et al. 2003; Yajima et al. 2006), although glucose uptake was not directly assessed in the present study. While calpastatin overexpression has been shown to improve glucose tolerance in early obesity in vivo (Muniappan et al. 2017), our in vitro data suggest that calpain inhibition could impair insulin responsiveness in human adipocytes. A limitation of the present study is that insulin‐stimulated glucose uptake was not directly measured, and, thus, additional studies will be required to confirm the functional consequences of calpeptin treatment.

Another indication of potential adverse effects on adipocyte metabolism was the downregulation of adiponectin at both mRNA and protein levels, along with reduced secretion into CM. To our knowledge, this is the first report linking calpain inhibition to decreased adiponectin, a key adipokine with anti‐diabetic, anti‐atherogenic, and anti‐inflammatory properties (Villarreal‐Molina and Antuna‐Puente 2012). Proteomic analyses of SGBS cells and EVs further revealed calpeptin‐induced changes in proteins associated with stress responses, including oxidative stress. Notably, HMOX1 was upregulated both in vitro and in ex vivo SAT cultures, suggesting activation of oxidative stress pathways. While murine studies support a protective HO‐1–adiponectin axis, with increased HO‐1 expression triggering adiponectin production (Bellner et al. 2019), our findings–consistent with others (Yang et al. 2015)–suggest a different relationship in human adipocytes, where increased HO‐1 is linked to insulin resistance, inflammation, and oxidative stress (Jais et al. 2014; Moreno‐Navarrete et al. 2017). Additional markers of stress included suppressed HP and TXN2, associated with hypoxia and hydrogen peroxide‐induced oxidative stress, respectively (Hao et al. 2024; He et al. 2020; Wang et al. 2007). The reduced expression of epoxide hydrolase 1 in calp EVs is also noteworthy, though its role in adipocyte biology remains unclear. In mouse 3T3‐L1 adipocytes, its downregulation has been linked to insulin resistance (Gautheron et al. 2021).

In addition to our SGBS in vitro findings, patient SAT ex vivo cultures also exhibited upregulation of HMOX1 at mRNA level. Although TXN2 and HP mRNA expression did not reach statistically significant differences, both showed negative correlations with patient BMI. Given the limited number of AT biopsies and the lack of corresponding protein‐level validation, these ex vivo analyses should be considered exploratory and interpreted with caution. Nevertheless, these findings suggest that the effects of calpeptin may be influenced by patient‐specific factors, such as adiposity. Although we were able to demonstrate anti‐inflammatory effects of calpeptin on SGBS adipocytes, several potentially deleterious phenomena also appeared, such as disturbed insulin signalling, reduced adiponectin expression, and signs of oxidative stress. Thus, our results further support the conclusion that calpeptin may not be a robust therapeutic strategy for counteracting obesity‐induced changes in human adipocytes. Future studies are needed to further investigate this contradiction with suppressed inflammatory responses.

However, it is important to acknowledge that the present study has several limitations. SGBS adipocytes are a cell line obtained from a single donor, which is why the effects of calpeptin need to be validated in future studies by using primary adipocytes from several donors. Moreover, 24‐h treatments may not be sufficient to monitor the long‐term effects of calpeptin on adipocyte metabolism. Although our AT ex vivo cultures offered a physiologically relevant model supplementing our SGBS adipocyte results, patient sample sizes were small, and all the AT biopsies were obtained from women. Thus, larger studies, also including both sexes are needed to validate our AT ex vivo results and correlations. Finally, in vivo studies are needed to confirm the physiological effects of calpeptin on AT biology.

Author Contributions

Johanna Matilainen: conceptualization; formal analysis; investigation; methodology; writing – original draft. Sanni Foster: investigation. Viivi Berg: investigation. Janne Tampio: methodology; investigation; formal analysis; writing – review & editing. Tanja Turunen: formal analysis; writing – review & editing. Anne‐Mari Mustonen: formal analysis; supervision; writing – review & editing. Heikki Kyykallio: investigation. Maija Vaittinen: methodology; supervision; writing – review & editing. Ville Männistö: methodology; supervision. Jussi Pihlajamäki: funding acquisition; supervision; PI of the KOBS study; writing – review & editing. Marjo Malinen: supervision. Sanna Oikari: supervision. Pirjo Käkelä: resources. Dorota Kaminska: investigation; formal analysis. Veera Luukkonen: investigation. Kristiina M. Huttunen: methodology; resources, writing – review & editing. Martin Wabitsch: resources. Petteri Nieminen: formal analysis; funding acquisition; supervision; writing – review & editing. Kirsi Rilla: funding acquisition; supervision; writing – review & editing.

Conflicts of Interest

The authors declare no conflicts of interest.

Supporting information

Supporting Information: jex270177‐sup‐0001‐TableS1‐S2.pdf

JEX2-5-e70177-s006.pdf (132.6KB, pdf)

Supporting Information: jex270177‐sup‐0002‐TableS3.xlsx

JEX2-5-e70177-s009.xlsx (71.4KB, xlsx)

Supporting Information: jex270177‐sup‐0003‐TableS4.xlsx

JEX2-5-e70177-s003.xlsx (790.9KB, xlsx)

Supporting Information: jex270177‐sup‐0004‐TableS5.xlsx

JEX2-5-e70177-s007.xlsx (19.2KB, xlsx)

Supporting Information: jex270177‐sup‐0005‐TableS6.xlsx

JEX2-5-e70177-s008.xlsx (36.2KB, xlsx)

Supporting Information: jex270177‐sup‐0006‐TableS7.xlsx

JEX2-5-e70177-s012.xlsx (22.6KB, xlsx)

Supporting Information: jex270177‐sup‐0007‐TableS8.xlsx

JEX2-5-e70177-s005.xlsx (340.1KB, xlsx)

Supporting Information: jex270177‐sup‐0008‐TableS9.xlsx

JEX2-5-e70177-s010.xlsx (12KB, xlsx)

Supporting Information: jex270177‐sup‐0009‐FigureS1.tiff

JEX2-5-e70177-s004.pdf (43.6KB, pdf)

Supporting Information: jex270177‐sup‐0010‐SuppMat.xlsx

JEX2-5-e70177-s002.xlsx (2.4MB, xlsx)

Supporting Information: jex270177‐sup‐0011‐SuppMat.docx

JEX2-5-e70177-s013.docx (46.5KB, docx)

Supporting Information: jex270177‐sup‐0012‐SuppMat.xlsx

JEX2-5-e70177-s001.xlsx (2.4MB, xlsx)

Supporting Information: jex270177‐sup‐0013‐FigureS2.tiff

JEX2-5-e70177-s011.tiff (9.6MB, tiff)

Acknowledgements

Matti Laitinen, Eija Rahunen, Johanna Kinnunen, Tero Sievänen, and Tiina Koivunen are acknowledged for their technical assistance, and Uma Thanigai‐Arasu for the valuable advice. Prof. Seppo Auriola is thanked for the valuable help on LC–MS proteomics analyses. The authors also thank the Cell and Tissue Imaging Unit at the Institute of Biomedicine for the confocal facilities and the EM facilities of the SIB Labs. Additionally, we appreciate Biocenter Finland and Biocenter Kuopio for supporting the LC–MS laboratory facility of the University of Eastern Finland. We also acknowledge CSC–IT Center for Science, Finland, for computational resources. Johanna Matilainen was supported by the Saastamoinen Foundation, University of Eastern Finland, salary‐funded UEF PhD position in Doctoral Programme in Molecular Medicine, Helena Vuorenmies Foundation (UEF), Magnus Ehrnrooth Foundation, Instrumentarium Science Foundation, and The Finnish Cultural Foundation. Martin Wabitsch received funding by the Federal Ministry of Research, Technology and Space (Bundesministerium für Forschung, Technologie und Raumfahrt, BMFTR) as part of the German Center for Child and Adolescent Health (DZKJ) under the funding code 01GL2407A. Kirsi Rilla was supported by the Research Council of Finland GeneCellNano Flagship (#337120).

Open access publishing facilitated by Itä‐Suomen yliopisto, as part of the Wiley ‐ FinELib agreement.

Data Availability Statement

All data generated or analysed during this study are included in this published article and its supplementary information files.

References

  1. Bellner, L. , Lebovics N. B., Rubinstein R., et al. 2019. “Heme Oxygenase‐1 Upregulation: A Novel Approach in the Treatment of Cardiovascular Disease.” Antioxidants & Redox Signaling 32: 1045–1060. 10.1089/ars.2019.7970. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Bond, S. T. , Calkin A. C., and Drew B. G.. 2022. “Adipose‐Derived Extracellular Vesicles: Systemic Messengers and Metabolic Regulators in Health and Disease.” Frontiers in Physiology 13: 837001–837001. 10.3389/fphys.2022.837001. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Camino, T. , Lago‐Baameiro N., Bravo S. B., et al. 2022. “Human Obese White Adipose Tissue Sheds Depot‐Specific Extracellular Vesicles and Reveals Candidate Biomarkers for Monitoring Obesity and Its Comorbidities.” Translational Research 239: 85–102. 10.1016/j.trsl.2021.01.006. [DOI] [PubMed] [Google Scholar]
  4. Camino, T. , Lago‐Baameiro N., Bravo S. B., et al. 2020. “Vesicles Shed by Pathological Murine Adipocytes Spread Pathology: Characterization and Functional Role of Insulin Resistant/Hypertrophied Adiposomes.” International Journal of Molecular Sciences 21: 2252. 10.3390/ijms21062252. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Catalano, M. , and O'Driscoll L.. 2020. “Inhibiting Extracellular Vesicles Formation and Release: A Review of EV Inhibitors.” Journal of Extracellular Vesicles 9: 1703244–1703244. 10.1080/20013078.2019.1703244. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Connolly, K. D. , Guschina I. A., Yeung V., et al. 2015. “Characterisation of Adipocyte‐Derived Extracellular Vesicles Released Pre‐ and Post‐Adipogenesis.” Journal of Extracellular Vesicles 4: 29159–29159. 10.3402/jev.v4.29159. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Durcin, M. , Fleury A., Taillebois E., et al. 2017. “Characterisation of Adipocyte‐Derived Extracellular Vesicle Subtypes Identifies Distinct Protein and Lipid Signatures for Large and Small Extracellular Vesicles.” Journal of Extracellular Vesicles 6: 1305677. 10.1080/20013078.2017.1305677. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Gautheron, F. , Georgievski A., Garrido C., and Quéré R.. 2023. “Bone Marrow‐Derived Extracellular Vesicles Carry the TGF‐β Signal Transducer Smad2 to Preserve Hematopoietic Stem Cells in Mice.” Cell Death Discovery 9: 117–117. 10.1038/s41420-023-01414-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Gautheron, J. , Morisseau C., Chung W. K., et al. 2021. “EPHX1 Mutations Cause a Lipoatrophic Diabetes Syndrome due to Impaired Epoxide Hydrolysis and Increased Cellular Senescence.” Elife 10: e68445. 10.7554/eLife.68445. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Grant, R. , Ansa‐Addo E., Stratton D., et al. 2011. “A Filtration‐Based Protocol to Isolate Human Plasma Membrane‐Derived Vesicles and Exosomes from Blood Plasma.” Journal of Immunological Methods 371: 143–151. 10.1016/j.jim.2011.06.024. [DOI] [PubMed] [Google Scholar]
  11. Hagberg, C. E. , and Spalding K. L.. 2024. “White Adipocyte Dysfunction and Obesity‐Associated Pathologies in Humans.” Nature Reviews Molecular Cell Biology 25: 270–289. 10.1038/s41580-023-00680-1. [DOI] [PubMed] [Google Scholar]
  12. Hao, X. , Liu M., Zhang X., et al. 2024. “Thioredoxin‐2 Suppresses Hydrogen Peroxide–Activated Nuclear Factor Kappa B Signaling via Alleviating Oxidative Stress in Bovine Adipocytes.” Journal of Dairy Science 107: 4045–4055. 10.3168/jds.2023-23465. [DOI] [PubMed] [Google Scholar]
  13. He, F. , Huang Y., Song Z., et al. 2020. “Mitophagy‐Mediated Adipose Inflammation Contributes to Type 2 Diabetes with Hepatic Insulin Resistance.” Journal of Experimental Medicine 218: e20201416–e20201416. 10.1084/jem.20201416. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Hotta, K. , Funahashi T., Arita Y., et al. 2000. “Plasma Concentrations of a Novel, Adipose‐Specific Protein, Adiponectin, in Type 2 Diabetic Patients.” Arteriosclerosis, Thrombosis, and Vascular Biology 20: 1595–1599. 10.1161/01.ATV.20.6.1595. [DOI] [PubMed] [Google Scholar]
  15. Hurwitz, S. N. , Conlon M. M., Rider M. A., Brownstein N. C., and Meckes D. G.. 2016. “Nanoparticle Analysis Sheds Budding Insights into Genetic Drivers of Extracellular Vesicle Biogenesis.” Journal of Extracellular Vesicles 5: 31295–31295. 10.3402/jev.v5.31295. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Huttenlocher, A. , Palecek S. P., Lu Q., et al. 1997. “Regulation of Cell Migration by the Calcium‐Dependent Protease Calpain.” Journal of Biological Chemistry 272: 32719–32722. 10.1074/jbc.272.52.32719. [DOI] [PubMed] [Google Scholar]
  17. Jais, A. , Einwallner E., Sharif O., et al. 2014. “Heme Oxygenase‐1 Drives Metaflammation and Insulin Resistance in Mouse and Man.” Cell 158: 25–40. 10.1016/j.cell.2014.04.043. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Kranendonk, M. E. G. , Visseren F. L. J., van Balkom B. W. M., et al. 2014. “Human Adipocyte Extracellular Vesicles in Reciprocal Signaling Between Adipocytes and Macrophages.” Obesity 22: 1296–1308. 10.1002/oby.20679. [DOI] [PubMed] [Google Scholar]
  19. Lazar, I. , Clement E., Attane C., Muller C., and Nieto L.. 2018. “A New Role for Extracellular Vesicles: How Small Vesicles Can Feed Tumors' Big Appetite.” Journal of Lipid Research 59: 1793–1804. 10.1194/jlr.R083725. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Lazar, I. , Clement E., Dauvillier S., et al. 2016. “Adipocyte Exosomes Promote Melanoma Aggressiveness Through Fatty Acid Oxidation: A Novel Mechanism Linking Obesity and Cancer.” Cancer Research 76: 4051–4057. 10.1158/0008-5472.CAN-16-0651. [DOI] [PubMed] [Google Scholar]
  21. Lee, J. E. , Moon P. G., Lee I. K., and Baek M. C.. 2015. “Proteomic Analysis of Extracellular Vesicles Released by Adipocytes of Otsuka Long‐Evans Tokushima Fatty (OLETF) Rats.” Protein Journal 34: 220–235. 10.1007/s10930-015-9616-z. [DOI] [PubMed] [Google Scholar]
  22. Letavernier, E. , Perez J., Bellocq A., et al. 2008. “Targeting the Calpain/Calpastatin System as a New Strategy to Prevent Cardiovascular Remodeling in Angiotensin II–Induced Hypertension.” Circulation Research 102: 720–728. 10.1161/CIRCRESAHA.107.160077. [DOI] [PubMed] [Google Scholar]
  23. Lin, Y. C. , Brown K., and Siebenlist U.. 1995. “Activation of NF‐κappa B Requires Proteolysis of the Inhibitor I κ B‐α: Signal‐Induced Phosphorylation of I κappa B‐alpha Alone Does Not Release Active NF‐κappa B.” Proceedings of the National Academy of Sciences 92: 552–556. 10.1073/pnas.92.2.552. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Matilainen, J. , Berg V., Vaittinen M., et al. 2024. “Increased Secretion of Adipocyte‐Derived Extracellular Vesicles Is Associated With Adipose Tissue Inflammation and the Mobilization of Excess Lipid in Human Obesity.” Journal of Translational Medicine 22: 623–623. 10.1186/s12967-024-05249-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. McDonald, M. C. , Mota‐Filipe H., Paul A., et al. 2001. “Calpain Inhibitor I Reduces the Activation of Nuclear Factor‐κΒ and Organ Injury/Dysfunction in Hemorrhagic Shock.” FASEB Journal 15: 171–186. 10.1096/fj.99-0645com. [DOI] [PubMed] [Google Scholar]
  26. McNamee, N. , Catalano M., Mukhopadhya A., and O'Driscoll L.. 2023. “An Extensive Study of Potential Inhibitors of Extracellular Vesicles Release in Triple‐Negative Breast Cancer.” BMC Cancer 23: 654–654. 10.1186/s12885-023-11160-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Moreno‐Navarrete, J. M. , Ortega F., Rodríguez A., et al. 2017. “HMOX1 as a Marker of Iron Excess‐Induced Adipose Tissue Dysfunction, Affecting Glucose Uptake and Respiratory Capacity inuman Adipocytes.” Diabetologia 60: 915–926. 10.1007/s00125-017-4228-0. [DOI] [PubMed] [Google Scholar]
  28. Muniappan, L. , Javidan A., Jiang W., et al. 2017. “Calpain Inhibition Attenuates Adipose Tissue Inflammation and Fibrosis in Diet‐Induced Obese Mice.” Scientific Reports 7: 14398–14398. 10.1038/s41598-017-14719-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Ni, K. , Wang C., Carnino J. M., and Jin Y.. 2020. “The Evolving Role of Caveolin‐1: A Critical Regulator of Extracellular Vesicles.” Medical Sciences 8: 46. 10.3390/medsci8040046. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Ogden, C. L. , Carroll M. D., Fryar C. D., and Flegal K. M.. 2015. “Prevalence of Obesity Among Adults and Youth: United States, 2011–2014.” NCHS Data Brief, no. 219: 1–8. [PubMed]
  31. Ouchi, N. , Parker J. L., Lugus J. J., and Walsh K.. 2011. “Adipokines in Inflammation and Metabolic Disease.” Nature Reviews Immunology 11: 85–97. 10.1038/nri2921. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Paul, D. S. , Harmon A. W., Winston C. P., and Patel Y. M.. 2003. “Calpain Facilitates GLUT4 Vesicle Translocation during Insulin‐Stimulated Glucose Uptake in Adipocytes.” Biochemical Journal 376: 625–632. 10.1042/bj20030681. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Pei, J. , Wang B., and Wang D.. 2022. “Current Studies on Molecular Mechanisms of Insulin Resistance.” Journal of Diabetes Research 2022: 1863429. 10.1155/2022/1863429. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Pihlajamäki, J. , Kuulasmaa T., Kaminska D., et al. 2012. “Serum Interleukin 1 Receptor Antagonist as an Independent Marker of Non‐Alcoholic Steatohepatitis in Humans.” Journal of Hepatology 56: 663–670. 10.1016/j.jhep.2011.10.005. [DOI] [PubMed] [Google Scholar]
  35. Reilly, S. M. , and Saltiel A. R.. 2017. “Adapting to Obesity With Adipose Tissue Inflammation.” Nature Reviews Endocrinology 13: 633–643. 10.1038/nrendo.2017.90. [DOI] [PubMed] [Google Scholar]
  36. Reinke, P. Y. A. , de Souza E. E., Günther S., et al. 2023. “Calpeptin Is a Potent Cathepsin Inhibitor and Drug Candidate for SARS‐CoV‐2 Infections.” Communications Biology 6: 1058–1058. 10.1038/s42003-023-05317-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Rome, S. , Blandin A., and Le Lay S.. 2021. “Adipocyte‐Derived Extracellular Vesicles: State of the Art.” International Journal of Molecular Sciences 22: 1788. 10.3390/ijms22041788. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Rosenow, A. , Arrey T. N., Bouwman F. G., et al. 2010. “Identification of Novel Human Adipocyte Secreted Proteins by Using SGBS Cells.” Journal of Proteome Research 9: 5389–5401. 10.1021/pr100621g. [DOI] [PubMed] [Google Scholar]
  39. Schey, K. L. , Grey A. C., and Nicklay J. J.. 2013. “Mass Spectrometry of Membrane Proteins: A Focus on Aquaporins.” Biochemistry 52: 3807–3817. 10.1021/bi301604j. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Shearwin‐Whyatt, L. M. , Brown D. L., Wylie F. G., Stow J. L., and Kumar S.. 2004. “N4WBP5A (Ndfip2), a Nedd4‐Interacting Protein, Localizes to Multivesicular Bodies and the Golgi, and Has a Potential Role in Protein Trafficking.” Journal of Cell Science 117: 3679–3689. 10.1242/jcs.01212. [DOI] [PubMed] [Google Scholar]
  41. Sreenan, S. K. , Zhou Y.‐P., Otani K., et al. 2001. “Calpains Play a Role in Insulin Secretion and Action.” Diabetes 50: 2013–2020. 10.2337/diabetes.50.9.2013. [DOI] [PubMed] [Google Scholar]
  42. Tabata, C. , Tabata R., and Nakano T.. 2010. “The Calpain Inhibitor Calpeptin Prevents Bleomycin‐Induced Pulmonary Fibrosis in Mice.” Clinical and Experimental Immunology 162: 560–567. 10.1111/j.1365-2249.2010.04257.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Trufelli, H. , Palma P., Famiglini G., and Cappiello A.. 2011. “An Overview of Matrix Effects in Liquid Chromatography–Mass Spectrometry.” Mass Spectrometry Reviews 30: 491–509. 10.1002/mas.20298. [DOI] [PubMed] [Google Scholar]
  44. Vaittinen, M. , Walle P., Kuosmanen E., et al. 2016. “FADS2 genotype Regulates Delta‐6 Desaturase Activity and Inflammation in Human Adipose Tissue.” Journal of Lipid Research 57: 56–65. 10.1194/jlr.M059113. [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. van Gerwen, J. , Shun‐Shion A. S., and Fazakerley D. J.. 2023. “Insulin Signalling and GLUT4 Trafficking in Insulin Resistance.” Biochemical Society Transactions 51: 1057–1069. 10.1042/BST20221066. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. van Niel, G. , D'Angelo G., and Raposo G.. 2018. “Shedding Light on the Cell Biology of Extracellular Vesicles.” Nature Reviews Molecular Cell Biology 19: 213–228. 10.1038/nrm.2017.125. [DOI] [PubMed] [Google Scholar]
  47. Villarreal‐Molina, M. T. , and Antuna‐Puente B.. 2012. “Adiponectin: Anti‐Inflammatory and Cardioprotective Effects.” Biochimie 94: 2143–2149. 10.1016/j.biochi.2012.06.030. [DOI] [PubMed] [Google Scholar]
  48. Visser, M. 1999. “Elevated C‐Reactive Protein Levels in Overweight and Obese Adults.” JAMA 282: 2131. 10.1001/jama.282.22.2131. [DOI] [PubMed] [Google Scholar]
  49. Wabitsch, M. , Brenner R. E., Melzner I., et al. 2001. “Characterization of a Human Preadipocyte Cell Strain with High Capacity for Adipose Differentiation.” International Journal of Obesity 25: 8–15. 10.1038/sj.ijo.0801520. [DOI] [PubMed] [Google Scholar]
  50. Walle, P. , Takkunen M., Männistö V., et al. 2016. “Fatty Acid Metabolism Is Altered in Non‐Alcoholic Steatohepatitis Independent of Obesity.” Metabolism 65: 655–666. 10.1016/j.metabol.2016.01.011. [DOI] [PubMed] [Google Scholar]
  51. Wang, B. , Wood I. S., and Trayhurn P.. 2007. “Dysregulation of the Expression and Secretion of Inflammation‐Related Adipokines by Hypoxia in Human Adipocytes.” Pflügers Archiv ‐ European Journal of Physiology 455: 479–492. 10.1007/s00424-007-0301-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Wang, P. , Mariman E., Renes J., and Keijer J.. 2008. “The Secretory Function of Adipocytes in the Physiology of White Adipose Tissue.” Journal of Cellular Physiology 216: 3–13. 10.1002/jcp.21386. [DOI] [PubMed] [Google Scholar]
  53. Wu, Y. , Yang H., Cheng M., et al. 2022. “Calpain Inhibitor Calpeptin Alleviates Ischemia/Reperfusion‐Induced Acute Kidney Injury via Suppressing AIM2 Inflammasome and Upregulating Klotho Protein.” Frontiers in Medicine 9: 811980. [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Yajima, Y. , Sato M., Sorimachi H., Inomata M., Maki M., and Kawashima S.. 2006. “Calpain System Regulates the Differentiation of Adult Primitive Mesenchymal ST‐13 Adipocytes.” Endocrinology 147: 4811–4819. 10.1210/en.2005-1647. [DOI] [PubMed] [Google Scholar]
  55. Yáñez‐Mó, M. , Siljander P. R.‐M., Andreu Z., et al. 2015. “Biological Properties of Extracellular Vesicles and Their Physiological Functions.” Journal of Extracellular Vesicles 4: 27066–27066. 10.3402/jev.v4.27066. [DOI] [PMC free article] [PubMed] [Google Scholar]
  56. Yang, M. , Kimura M., Ng C., et al. 2015. “Induction of Heme‐Oxygenase‐1 (HO‐1) Does Not Enhance Adiponectin Production in H‐uman Adipocytes: Evidence against a Direct HO‐1 – Adiponectin Axis.” Molecular and Cellular Endocrinology 413: 209–216. 10.1016/j.mce.2015.06.034. [DOI] [PubMed] [Google Scholar]
  57. Yoshida, M. , Miyasaka Y., Ohuchida K., et al. 2016. “Calpain Inhibitor Calpeptin Suppresses Pancreatic Cancer by Disrupting Cancer–Stromal Interactions in a Mouse Xenograft Model.” Cancer Science 107: 1443–1452. 10.1111/cas.13024. [DOI] [PMC free article] [PubMed] [Google Scholar]
  58. Yousefi, S. , Perozzo R., Schmid I., et al. 2006. “Calpain‐Mediated Cleavage of Atg5 Switches Autophagy to Apoptosis.” Nature Cell Biology 8: 1124–1132. 10.1038/ncb1482. [DOI] [PubMed] [Google Scholar]
  59. Zhang, Y. , Yu M., Dong J., Wu Y., and Tian W.. 2020. “Identification of Novel Adipokines through Proteomic Profiling of Small Extracellular Vesicles Derived from Adipose Tissue.” Journal of Proteome Research 19: 3130–3142. 10.1021/acs.jproteome.0c00131. [DOI] [PubMed] [Google Scholar]
  60. Zuo, J. , Hu Z., Liu T., et al. 2018. “Calpeptin Attenuates Cigarette Smoke‐Induced Pulmonary Inflammation via Suppressing Calpain/IκBα Signaling in Mice and BEAS‐2B Cells.” Pathology ‐ Research and Practice 214: 1199–1209. 10.1016/j.prp.2018.06.019. [DOI] [PubMed] [Google Scholar]

Associated Data

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

Supplementary Materials

Supporting Information: jex270177‐sup‐0001‐TableS1‐S2.pdf

JEX2-5-e70177-s006.pdf (132.6KB, pdf)

Supporting Information: jex270177‐sup‐0002‐TableS3.xlsx

JEX2-5-e70177-s009.xlsx (71.4KB, xlsx)

Supporting Information: jex270177‐sup‐0003‐TableS4.xlsx

JEX2-5-e70177-s003.xlsx (790.9KB, xlsx)

Supporting Information: jex270177‐sup‐0004‐TableS5.xlsx

JEX2-5-e70177-s007.xlsx (19.2KB, xlsx)

Supporting Information: jex270177‐sup‐0005‐TableS6.xlsx

JEX2-5-e70177-s008.xlsx (36.2KB, xlsx)

Supporting Information: jex270177‐sup‐0006‐TableS7.xlsx

JEX2-5-e70177-s012.xlsx (22.6KB, xlsx)

Supporting Information: jex270177‐sup‐0007‐TableS8.xlsx

JEX2-5-e70177-s005.xlsx (340.1KB, xlsx)

Supporting Information: jex270177‐sup‐0008‐TableS9.xlsx

JEX2-5-e70177-s010.xlsx (12KB, xlsx)

Supporting Information: jex270177‐sup‐0009‐FigureS1.tiff

JEX2-5-e70177-s004.pdf (43.6KB, pdf)

Supporting Information: jex270177‐sup‐0010‐SuppMat.xlsx

JEX2-5-e70177-s002.xlsx (2.4MB, xlsx)

Supporting Information: jex270177‐sup‐0011‐SuppMat.docx

JEX2-5-e70177-s013.docx (46.5KB, docx)

Supporting Information: jex270177‐sup‐0012‐SuppMat.xlsx

JEX2-5-e70177-s001.xlsx (2.4MB, xlsx)

Supporting Information: jex270177‐sup‐0013‐FigureS2.tiff

JEX2-5-e70177-s011.tiff (9.6MB, tiff)

Data Availability Statement

All data generated or analysed during this study are included in this published article and its supplementary information files.

Articles from Journal of Extracellular Biology are provided here courtesy of Wiley on behalf of the International Society for Extracellular Vesicles (ISEV)

RESOURCES