Proteomic analysis of urinary extracellular vesicles highlights specific signatures for patients with primary aldosteronism

Abstract

Background: Urinary extracellular vesicles (uEVs) can be released by different cell types facing the urogenital tract and are involved in cellular trafficking, differentiation and survival. UEVs can be easily detected in urine and provide pathophysiological information "in vivo" without the need of a biopsy. Based on these premises, we hypothesized that uEVs proteomic profile may serve as a valuable tool in the differential characterization between Essential Hypertension (EH) and primary aldosteronism (PA).

Methods: Patients with essential hypertension (EH) and PA were enrolled in the study (EH= 12, PA=24: 11 Bilateral Primary Aldosteronism subtype (BPA) and 13 Aldosterone Producing Adenoma (APA)). Clinical and biochemical parameters were available for all the subjects. UEVs were isolated from urine by ultracentrifugation and analysed by Transmission Electron Microscopy (TEM) and nanotrack particle analysis (NTA). UEVs protein content was investigated through an untargeted MS-based approach. Statistical and network analysis was performed to identify potential candidates for the identification and classification of PA.

Results: MS analysis provided more than 300 protein identifications. Exosomal markers CD9 and CD63 were detected in all samples. Several molecules characterizing EH vs PA patients as well as BPA and APA subtypes were identified after statistical elaboration and filtering of the results. In particular, some key proteins involved in water reabsorption mechanisms, such as AQP1 and AQP2, were among the best candidates for discriminating EH vs PA, as well as A1AG1 (AGP1).

Conclusion: Through this proteomic approach, we identified uEVs molecular indicators that can improve PA characterization and help in the gain of insights of the pathophysiological features of this disease. In particular, PA was characterized by a reduction of AQP1 and AQP2 expression as compared with EH.

Keywords: aquaporin 1 (AQP1); aquaporin 2 (AQP2); essential hypertension; primary aldosteronism; proteomics; urinary extracellular vesicles (UEVs); α-1-acid glycoprotein (AGP1).

Figure 1

UEVs characterization: (A) representative picture of TEM characterization of uEVs isolated with ultracentrifugation; (B, C) NTA data on size (B, C) concentration of uEVs, error bar shows the range. UEVs concentration in relation to urinary creatinine (D) and protein content of the isolated vesicles (E).

Figure 2

Proteins regulated in the EH vs PA comparison. Proteins resulting from the comparison EH vs PA are represented in: (A) Barplot of Log(fc) of statistically significant proteins (Pval < 0.05) with a fold change cutoff of 1.5. Proteins more abundant in EH are indicated as orange bars and proteins more abundant in PA as blue bars. (B) Volcano plot and distribution of P values and fold change of the positive hits; (C) Heatmap of the top 26 proteins resulting from t-test analysis. Each protein is indicated with its abbreviated gene name alias.

Figure 3

Candidate biomarkers for the EH vs PA comparison. (A) Boxplots and relative position in the nephron of AQP1, AQP2 (B) ROC curves of the best hits in the comparison according to AUC and (C) target Multi ROC combining the best 6 selected features (AUC≥0.80).

Figure 4

Proteins regulated in the APA vs BPA comparison. Proteins resulting from the comparison between the 2 PA subtypes are represented in: (A) Barplot of Log(fc) for proteins with Pval < 0.05 and FC cutoff of 1.5. Proteins more abundant in APA are indicated as blue bars and proteins more abundant in BPA are indicated asgreen bars; (B) Volcano plot and distribution of P values and fold change of the positive hits; (C) Heatmap of the top 25 proteins resulting from t-test analysis. Each protein is indicated with its abbreviated gene name alias.

Figure 5

ROC curves of candidate biomarkers for the comparison APA vs BPA. ROC curves of the best hits in the comparison between the 2 APA subtypes (A, B) multi ROC curves obtained combining the best 6 selected features (AUC≥0.84).