Obesity has differential impacts on the protein profiles of the EAT-Exos, VAT-Exos and SAT-Exos
We used C57BL/6J mice to set up HFD-induced obesity (DIO) mouse model (Fig. 1a), control diet (CD) served as control. Exosomes derived from epididymal adipose tissue (EAT-Exos), visceral adipose tissue (VAT-Exos) and subcutaneous adipose tissue (SAT-Exos) were purified. Transmission electron microscopy (TEM) indicated the circulating exosomes were intact (Fig. 1b). TEM (Fig. 1b) and dynamic light scattering (DLS) (Fig. 1c) showed that the diameters of the purified exosomes were consistent with the proposed sizes of exosomes (30-150nm) [1]. Authenticity of the purified exosomes was also examined by the expressions of exosomal markers CD63 and CD81 (Fig. 1d).
iTRAQ-based quantitative proteomic was used to examine the protein profiles of the EAT-Exos, VAT-Exos and SAT-Exos under obesity conditions. Data showed that a total of 304 proteins were upregulated and 186 were downregulated in EAT-Exos (Fig. 1e); while 306 exosomal proteins were upregulated, and 273 were downregulated in VAT-Exos (Fig. 1f). Interestingly, HFD feeding had a great impact on the protein profiles in the SAT-Exos as 578 proteins were upregulated, and 310 proteins were downregulated (Fig. 1g). Besides, we also compared the exosomal protein profiles in SAT-Exos, VAT-Exos and EAT-Exos under CD and HFD conditions as shown in the supplementary Fig. 1a to 1f. With eukaryotic orthologous groups (KOG) annotation, we found that the detected proteins in these exosomes were mainly involved in cellular process and participate in binding activity (supplementary Fig. 1g). Among different cellular processes, 13.46% of the exosomal proteins was involved in metabolic process (Table 1) including carbohydrate metabolism, amino acid metabolism and lipid metabolism (Fig. 1h).
Obesity affects the metabolic associated proteins in SAT-Exos and VAT-Exos
We next compared the impacts of obesity on the protein profiles of EAT-Exos, VAT-Exos and SAT-Exos. Proteins with 1.2-fold change and Q-value less than 0.05 were determined as differentially expressed proteins (DEPs). Most of the DEPs in the EAT-Exos, VAT-Exos and SAT-Exos were cytosolic proteins (Supplementary Fig. 2a-c). Although pathway enrichment analysis showed that DEPs in EAT-Exos, VAT-Exos and SAT-Exos were mainly involved in metabolism (Fig. 2a to 2c), only 239 and 375 metabolic-associated DEPs were identified in EAT-Exos (Supplementary Fig. 2d) and VAT-Exos (Supplementary Fig. 2e), respectively, while 544 metabolic-associated DEPs were identified in SAT-Exos (Supplementary Fig. 2f). Scatter plot of the top 20 of KEGG enrichment RichFactor also revealed that DEPs in EAT-Exos were not only associated to metabolism but other biological processes (Fig. 2d). As shown in Fig. 2e, DEPs in EAT-Exos also involved in calcium metabolism, endocytosis, bacterial invasion and cortisol synthesis and secretion, endocrine and other factor-regulated calcium reabsorption, peroxisome and fatty acid metabolism. DEPs in VAT-Exos were mainly involved in complement and coagulation cascades, pathogenic bacterial infection and metabolism (Fig. 2f and 2g). Interestingly, DEPs in SAT-Exos were mostly involved in metabolism (Fig. 2h) with a few DEPS involved in proteasome (Fig. 2i).
Furthermore, we performed STRING to predict the protein-protein interaction (PPI) of the DEPs, which include both physical and functional associations. Interestingly, PPI of the DEPs in SAT-Exos predicted that the upregulated DEPs mainly interacted with other DEPs that were upregulated, and downregulated DEPS with other DEPs that were downregulated, which was different from those in EAT-Exos and VAT-Exos (Supplementary Fig. 2g to 2i) We postulated that such DEPs interactions in SAT-Exos would exacerbate the pathological effects of the DEPs under obesity conditions. Supplementary Table 1a to 1c separately highlighted the DEPs in EAT-Exos, VAT-Exos and SAT-Exos that were increased by at least 2-fold or reduced by at least 0.3-fold. The results clearly showed that obesity had differential effects on the exosomal proteins in these exosomes.
Our proteomic data strongly suggest that obesity has a more prominent effect on the proteins in the SAT-Exos than those in EAT-Exos and VAT-Exos. The DEPs in the EAT-Exos are involved in different biological functions, while those in SAT-Exos and VAT-Exos are mainly involved in metabolism.
Untargeted metabolomics analysis reveals a significant impact of SAT-Exos on the serum metabolite profile in constitutive Rab27a knockout mouse model
Since the DEPs in SAT-Exos and VAT-Exos under obesity conditions are mainly related to metabolism, we next examined whether these exosomes would affect the metabolic profiles in the mice. To eliminate the effects of endogenous exosomes on the mouse metabolism, we used constitutive Rab27a knockout mouse (B6/J-Rab27a-Cas9-KO) as model. Rab27a is critical for exosome secretion [22]. More importantly, Rab27a-KO mice are shown to have reduced exosome secretion [23]. We first preformed genotyping to validate the knockout of Rab27a in the B6/J-Rab27a-Cas9-KO mice. As shown in Fig. 3a, only homozygous knockout mice were selected for the study. Knockout of Rab27a protein in adipose tissues, heart, liver, spleen, lung, kidney and brain was confirmed by Western blot analysis (Fig. 3b). These data clearly demonstrated that Rab27a, the critical protein for exosome secretion, was successfully knockout in the B6/J-Rab27a-Cas9-KO mice.
Then, we separately injected equal amount of EAT-Exos, VAT-Exos and SAT-Exos that were purified from DIO mice into the B6/J-Rab27a-Cas9-KO mouse models. The injections were done twice a week for 2 weeks. Then, we employed untargeted metabolomics analysis to examine the changes of the metabolite profiles in these mice. The quality of the samples was tested by chromatogram overlapping (Supplementary Fig. 3a and 3b) and the coefficient of variation (CV) of the relative peak area in the samples (Supplementary Fig. 3c). In the metabolomic study, a total of 1858 compounds were detected in the positive mode, and 739 of them with known identification. In the negative mode, only 561 compounds were detected and 315 of them with known identification. Most of the metabolites detected in the positive mode are amino acids and organic acids in the negative mode. KEGG database was used to annotate the identified metabolites to understand their biological functions. As shown in supplementary Fig. 3d and 3e, most of the metabolites were involved in amino acid metabolism, followed by lipid metabolism, carbohydrate metabolism and metabolism of cofactors and vitamins.
To examine the differential effects of the EAT-Exos, VAT-Exos and SAT-Exos on metabolism, we employed PLS-DA to examine the distribution and separation trend of the metabolite samples. PLS-DA provides high rates of sensitivity and specificity as it can substantially reduce the number of discriminatory variables by creating significant VIP (variable importance in projection) scores. As revealed by the models, injections of SAT-Exos, EAT-Exos or VAT-Exos that were purified from DIO mice had significant impacts on the plasma metabolites in the B6/J-Rab27a-Cas9-KO mice as illustrated by the sample clustering in the PLS-DA (Supplementary Fig. 3f to 3h).
We next screened for the differential metabolites in different grouping pairs of the mice. In the screening process, VIP of the first two PCs of the PLS-DA model above 1 was set as one of the criteria; and only metabolites with fold changes ≥ 1.2 or ≤ 0.83 that reached statistical significance (p-value < 0.05) were shortlisted. As shown in Supplementary Table 2, EAT-Exos injection significantly affected a total of 47 metabolites, VAT-Exos injection affected a total of 62 metabolites, and SAT-Exos injection affected a total of 153 metabolites in both positive and negative modes. The metabolites detected in these mice were listed in Supplementary Table 3a to 3f. The visual displays of the differential metabolites were shown in the respective volcano plots (Fig. 3c to 3e) and heat maps (Supplementary Fig. 3i to 3k).
In the pathway enrichment analysis, only the metabolites that showed statistically significant differences between groups were shortlisted for the analysis. The number of affected pathways after EAT-Exos injection was less than those after VAT-Exos and SAT-Exos injections (Table 2a). Interestingly, injection of SAT-Exos affected many metabolic pathways in the mice with a total of 1706 differential metabolites involved (Table 2a).
We next correlated these differential metabolites after SAT-Exos injection with the DEPs in the SAT-Exos that were increased by at least 2-fold or reduced by 0.3-fold as listed in supplementary Table 1c. We found that these DEPs were correlated to 14 different metabolisms including amino acid metabolisms and lipid metabolisms such as fatty acid degradation and steroid hormone biosynthesis (Fig. 3f).
Taken together, the metabolomic data suggest that the effects of EAT-Exos, VAT-Exos and SAT-Exos on the mouse metabolism are different. Injection of SAT-Exos has more prominent effects on the metabolite profiles and metabolic pathways in the B6/J-Rab27a-Cas9-KO mice when compared to EAT-Exos and VAT-Exos injections. Correlation analysis suggest that DEPs in the SAT-Exos contribute to the metabolic changes including amino acid metabolism and fatty acid metabolism under obesity conditions.
Untargeted lipidomics analysis reveals the impacts of SAT-Exos on the plasma lipid profile in B6/J-Rab27a-Cas9-KO mouse model, which mimic the plasma lipid profile in DIO mice
Since our data suggest that SAT-Exos that are derived from DIO mice have significant effects on fatty acid metabolism, we next performed global lipidomics to compare the plasma lipid profiles between the DIO mice and the B6/J-Rab27a-Cas9-KO mice after SAT-Exos injection.
After injecting SAT-Exos purified from DIO mice into the B6/J-Rab27a-Cas9-KO mice twice a week for two weeks, we performed untargeted lipidomics with the plasma lipid samples. The base peak chromatograms (BPC) of the lipid samples in positive and negative modes were shown in supplementary Fig. 4a and 4b. The CV distribution of lipid molecules was shown in supplementary Fig. 4c. Multivariate statistical analysis and univariate analysis were used to screen different lipids between groups. PLS-DA models showed the separation trends of the different plasma lipid samples, suggesting that SAT-Exos affects the plasma lipid profiles (supplementary Fig. 4d). Interestingly, SAT-Exos injection reduced the triglyceride (TG) levels, it also changed the levels of glycerophospholipids in the plasma in these mice (supplementary Table 4). Subsequent pathway enrichment analysis also suggests that SAT-Exos affects metabolism such as arachidonic acid metabolism, linoleic acid metabolism, biosynthesis of unsaturated fatty acid, regulation of lipolysis in adipocytes and glycerophospholipid metabolism in the mice (Table 2b).
Next, we examined the lipid profiles in the DIO mice, with CD mice served as control. The BPC of the lipid samples in the DIO and CD mice were shown in Supplementary Fig. 5a to 5d. In the lipidomic study, a total of 667 lipid molecules were detected. The lipid profiles for HFD and CD mice were different as indicated by the lipid sample clustering in the PCA (Supplementary Fig. 5e). The differences in the lipid molecules between these mice were visualized in volcano plots (Fig. 4a) and the heat maps (supplementary Fig. 5f), respectively.
Interestingly, our previous data showed that injection of SAT-Exos purified from DIO mice into the B6/J-Rab27a-Cas9-KO mice reduced TG levels and changed glycerophospholipid levels. DIO mice also exhibited similar changes when compared to CD mice. As shown in Fig. 4b, in the DIO mice, the levels of fatty acid (FA), diglyceride (DG), monoglyceride and diglyceride (MGDG) were increased, while TG level was reduced (Fig. 4b), which suggest a change in the lipolytic activity. Besides, SAT-Exos also changed the plasma levels of glycerophospholipids including dimethylphosphatidylethanolamine (dMePH), phosphatidylserine (PS), lyso-phosphatidylglycerol (LPG), phosphatidylglycerol (PG), lyso-phosphatidylcholine (LPC), phosphatidylethanolamine (PE), lyso-phosphatidylethanolamine (LPE), phosphatidylglycerol (PI), lysodimethylphosphatidylethanolamine (LdMePE) and phosphatidylcholine (PC).