METHOD FOR PREDICTING PROANGIOGENIC POTENTIAL OF EXTRACELLULAR VESICLES (EVS)

Abstract

The present invention relates to an in vitro method for predicting the proangiogenic activity of preparations of extracellular vesicles (EVs), preferably blood-derived EVs, wherein the method is based on the combined determination of the content of transforming growth factor beta (TGFβ) and microRNA-130a. Also disclosed is a method of manufacturing a preparation of extracellular vesicles (EVs) predicted to have strong proangiogenic activity and the EVs preparations thereof, which are effective for the therapeutic treatment of ischemic diseases, ischemic injuries and pathological conditions associated with risk of cardiovascular disease, or for use in wound healing.

Claims

1. A method for predicting whether a composition of extracellular vesicles (EVs) has proangiogenic activity, the method comprising the steps of: (a) quantifying the miR-130a microRNA content in the composition of EVs, (b) quantifying the transforming growth factor beta (TGFβ) content in the composition of EVs; and (c) determining whether the miR-130a content is above a first predetermined value and the TGFβ content is above a second predetermined value, wherein: when the miR-130a content is above said first predetermined value and the TGFβ content is above said second predetermined value, the composition of EVs is predicted to have proangiogenic activity.

2. The method according to claim 1, wherein the miR-130a content is quantified as a Ct value by real-time polymerase chain reaction (real-time PCR) and wherein there is an inverse correlation between the miR-130a content and the Ct value.

3. The method according to claim 2, wherein said first predetermined value is a Ct value<30.

4. The method according to claim 1, wherein the TGFβ content is measured by an immunoassay.

5. The method according to claim 1, wherein said second predetermined value is an amount of TGFβ of 23 pg/10.sup.10 EVs.

6. The method according to claim 1, further comprising the step of (d) quantifying the proangiogenic activity of the composition of EVs by means a potency test which comprises the following steps: testing the composition of EVs by a BrdU cell proliferation assay to obtain a composition value; testing a negative control by the BrdU cell proliferation assay to obtain a negative control value; testing a positive control by the BrdU cell proliferation assay to obtain a positive control value; and calculating the % proangiogenic activity of the composition of EVs in the BrdU cell proliferation assay by applying the following formula: $\%\mathrm{proangiogenic}\mathrm{activity}=\frac{\mathrm{composition}\mathrm{value}-\mathrm{negative}\mathrm{control}\mathrm{value}}{\mathrm{positive}\mathrm{crtl}\mathrm{value}-\mathrm{negative}\mathrm{control}\mathrm{value}}\times 100.$

7. The method according to claim 1, further comprising the step of (d) of quantifying the proangiogenic activity of the composition of EVs by a potency test which comprises the following steps: testing the composition of EVs by a tubulogenesis assay to obtain a composition value; testing a negative control by the tubulogenesis assay to obtain a negative control value; testing a positive control by the tubulogenesis assay to obtain a positive control value; and calculating the % proangiogenic activity of the composition of EVs in the tubulogenesis assay by applying the following formula: $\%\mathrm{proangiogenic}\mathrm{activity}=\frac{\mathrm{composition}\mathrm{value}-\mathrm{negative}\mathrm{control}\mathrm{value}}{\mathrm{positive}\mathrm{crtl}\mathrm{value}-\mathrm{negative}\mathrm{control}\mathrm{value}}\times 100.$

8. The method according to claim 1, further comprising the step of (d) quantifying the proangiogenic activity of the composition of EVs by a potency test comprising testing the composition of EVs by a BrdU cell proliferation assay to obtain a composition value; testing a negative control by the BrdU cell proliferation assay to obtain a negative control value; testing a positive control by the BrdU cell proliferation assay to obtain a positive control value; and calculating the % proangiogenic activity of the composition of EVs in the BrdU cell proliferation assay by applying the following formula: $\%\mathrm{proangiogenic}\mathrm{activity}=\frac{\mathrm{composition}\mathrm{value}-\mathrm{negative}\mathrm{control}\mathrm{value}}{\mathrm{positive}\mathrm{crtl}\mathrm{value}-\mathrm{negative}\mathrm{control}\mathrm{value}}\times 100$ and testing the composition of EVs by a tubulogenesis assay to obtain a composition value; testing a negative control by the tubulogenesis assay to obtain a negative control value; testing a positive control by the tubulogenesis assay to obtain a positive control value; and calculating the % proangiogenic activity of the composition of EVs in the tubulogenesis assay by applying the following formula: $\%\mathrm{proangiogenic}\mathrm{activity}=\frac{\mathrm{composition}\mathrm{value}-\mathrm{negative}\mathrm{control}\mathrm{value}}{\mathrm{positive}\mathrm{crtl}\mathrm{value}-\mathrm{negative}\mathrm{control}\mathrm{value}}\times 100.$

9. The method according to claim 1, wherein the composition of EVs has a proangiogenic activity of at least 50%.

10. The method according to claim 1, wherein the EVs are from human cells.

11. A method for manufacturing a preparation of proangiogenic extracellular vesicles (EVs), the method comprising the steps of: isolating EVs from multiple preparations of a body fluid or from a conditioned medium of a cell culture; preparing one or more samples from the isolated EVs at a predetermined concentration of EVs; predicting the proangiogenic activity of each EVs sample with a method for predicting whether a composition of extracellular vesicles (EVs) has proangiogenic activity, said method comprising quantifying the miR-130a microRNA content in the composition of EVs, quantifying the transforming growth factor beta (TGFβ) content in the composition of EVs, and determining whether the miR-130a content is above a first predetermined value and the TGFβ content is above a second predetermined value. wherein: when the miR-130a content is above said first predetermined value and the TGFβ content is above said second predetermined value, the composition of EVs is predicted to have proangiogenic activity; selecting the samples in which miR-130a content is above said first predetermined value, and TGFβ is above said second predetermined value; and optionally pooling two or more of active EVs samples, thereby obtaining a preparation of proangiogenic EVs.

12. The method according to claim 11, wherein the miR-130a content is quantified as a Ct value by real-time PCR and said first predetermined value is a Ct value<30.

13. The method according to claim 11, wherein said second predetermined value is a TGFβ amount of 23 pg/10.sup.10 EVs.

14. The method according to claim 11, wherein the preparation of proangiogenic EVs has a proangiogenic activity of at least 50%.

15. The method according to claim 11, wherein the EVs are from human cells.

16. A method for the therapeutic treatment of a disease or injury positively influenced by proangiogenic therapy or for wound healing in a subject in need thereof, said method comprising administering to said subject a preparation of proangiogenic extracellular vesicles (EVs) obtainable by the method according to claim 11, wherein the miR-130a content in the preparation measured as Ct value by real-time PCR is Ct<30 and the TGFβ content in the preparation is >23 pg/10.sup.10 EVs.

17. A method for the therapeutic treatment of a disease or injury positively influenced by proangiogenic therapy or for wound healing in a subject in need thereof, said method comprising administering to said subject a preparation of proangiogenic extracellular vesicles (EVs) having a miR-130a content measured as Ct value by real-time PCR of Ct<30 and/or a TGFβ content>23 pg/10.sup.10 EVs.

18. The method according to claim 16, wherein the preparation of proangiogenic EVs has a proangiogenic activity of at least 50%.

19. The method according to claim 16, wherein the EVs are derived from a biological fluid or from a conditioned cell or tissue culture medium.

20. The method according to claim 19, wherein the biological fluid is whole blood, plasma, or serum.

21. The method according to claim 20, wherein the EVs are prepared from serum of a healthy donor or from serum of a patient with cardiovascular risk factors.

22. The method according to claim 16, wherein the disease or injury is a vascular disease or injury, or a condition associated with risk of cardiovascular disease.

23. The method according to claim 16, wherein the disease or injury is selected from the group consisting of acute myocardial infarction, acute cerebrovascular disease, acute and chronic peripheral artery disease, acute kidney ischemia, obesity and diabetes mellitus.

24. The method according to claim 4, wherein the TGFβ content is measured by an enzyme-linked immunosorbent assay (ELISA).

Description

[0086] The invention will be better understood from the following examples which are provided by way of illustration only and which make reference to the appended drawings, wherein:

[0087] FIG. 1 shows the results of sEV characterization by Nanosight. (A) Representative images of NTA analysis referred to individual groups of patients. (B) Dot plot graph representing NTA size distribution, with mean size value for each individual subject (healthy donor, obese, diabetic, diabetic/obese and ischemic patient). (C) Histogram reporting the number of EVs recovered from serum from individual groups of patients. D=diabetic; O=Obese; OD=obese/diabetic; IC=Ischemic patients. *p<0.05 obese and ischemic patients vs healthy subjects; (One-way ANOVA followed by Multiple Comparison Test) (n=9 patients/group).

[0088] FIG. 2 shows the in vitro and in vivo pro-angiogenic activity of serum EVs from healthy donors and patients (A) Representative micrographs showing vessels formation in response to effective and ineffective sEVs. Each number refers to sEVs prepared from an individual subject (upper panel=ineffective sEVs; lower panel=effective sEVs) (n=3 each group, except for OD the same sample was used in 3 independent experiments). (B) Results of in vivo quantitative analysis of vessel formation. For each experimental condition, vessels were counted in 10 sections of Matrigel. Data show the average number of vessels counted in untreated mice (C) (n=3) or in mice treated with the following preparations of EVs: proangiogenic ineffective sEVs from healthy donors (i-sEVs), proangiogenic effective sEVs from healthy donors (e-sEVs); proangiogenic ineffective sEVs from diabetic patients (D i-sEVs), proangiogenic effective sEVs from diabetic patients (D e-sEVs); proangiogenic ineffective sEVs from obese patients (O i-sEVs), proangiogenic effective sEVs from obese patients (O e-sEVs); proangiogenic ineffective sEVs from diabetic/obese patients (OD i-sEVs), proangiogenic effective sEVs from diabetic/obese patients (OD e-sEVs); proangiogenic ineffective sEVs from ischemic patients (IC i-sEVs), proangiogenic effective sEVs from ischemic patients (IC e-sEVs). *p<0.05 healthy e-sEV vs. healthy i-sEV; § p<0.05 diabetic e-sEV vs. diabetic i-sEV; #p<0.05 obese e-sEV vs. obese i-sEV; °p<0.05 diabetic/obese e-sEV vs. diabetic/obese i-sEV; +p<0.05 ischemic e-sEV vs. ischemic i-sEV ischemic; (One-way ANOVA followed by Multiple Comparison Test). (n=3 each group except for OD the same sample was used in 3 independent experiments). (Original magnification: ×200; scale bar: 12 μm).

[0089] FIG. 3 shows that the proangiogenic activity of sEVs correlates with their TGFβ content. The graphs report the data obtained for samples of serum EVs prepared from individual subjects in each group (healthy donors, diabetic, obese and ischemic patients). For each group of patients, the upper curve is referred to the TGFβ content measured in sEVs as pg/10.sup.10 EVs, while the lower curve is referred to the % of proangiogenic activity as measured in the in vitro potency test. The dotted line indicates the cut-off of TGFβ>23 pg/10.sup.10 EVs for proangiogenic effective and ineffective sEVs. Each number corresponds to an individual patient (n=3 each group).

[0090] FIG. 4 shows the results of miRNAs expression profiling in sEVs. (A) Distribution of Ct values measured for miR-130a in proangiogenic effective (dark circles) and ineffective (white circles) sEVs from individual patients and healthy subjects. Results are reported as 40-Ct. (B) Network analysis of pathways positively correlated with miR-130a. Data were obtained by DIANA miRpath analysis. Only pathways including at least 15 genes were selected.

[0091] FIG. 5 (A) Network analysis between miR-130a and mRNA targets. Lines represent interactions between genes and miR-130a predicted by the IPA Software: indirect interactions (dotted lines), direct interactions (continuous lines). Squares include TGFβ and TGFBR. Circles include genes involved in angiogenesis (KDR, EPHB6, ROCK1, HOXA5). (B) Receiving Operating Characteristic (ROC) curves and the corresponding area under the curve (AUC) show that miR-130a and TGFβ have predictive ability to discriminate proangiogenic effective sEVs from ineffective vesicles. For ROC analysis, the results obtained for sEVs from all patients and healthy subjects were considered. The AUC values as well as standard errors, p-values, and threshold values are reported in the tables below the ROC curves.

EXAMPLES

1. Method

1.1 Patients

[0092] In the study carried out by the present inventors, thirty-six patients were included with cardiovascular risk factors and nine sex-matched healthy volunteers. In particular, nine diabetic patients (D: n=9), nine obese patients (O: n=9), nine diabetic and obese patients (OD: n=9), and nine ischemic patients (patients undergoing to surgical treatment for hind limb ischemia) (IC: n=9) were examined. All diabetic patients were not treated with insulin. All human experiments were performed in accordance with European Guidelines and policies and approved by the Ethical Committee of the University of Turin, Italy. Serum from all patients was obtained after admission to the Clinics (D, O, OD) and before surgery for ischemic patients (IC). Informed consent was obtained from all patients. Human serum from healthy donors (n=9) was provided by the Blood Bank of “Città della Salute e della Scienza di Torino”, after informed consent and approval by the internal Review Board of the Blood Bank.

1.2. Study Approval

[0093] Animal studies were conducted in accordance with the Italian National Institute of Health Guide for the Care and Use of Laboratory Animals (protocol no: 490/2105-PR). Mice were housed according with the Federation of European Laboratory Animal Science Association Guidelines and the Ethical Committee of the University of Turin. All experiments were performed in accordance with relevant guidelines and regulations.

1.3. Serum EVs Isolation

[0094] Human blood was obtained from healthy and patients donors by venipuncture. A total of 9 ml serum each donor were recovered from each donor and stored at −80° C. After thawing, total EVs were isolated and purified by Ultracentrifugation at 100,000×g for 2 h preceded by a centrifugation at 3000 g to remove debris. Pellets were washed once with PBS and centrifuged at 100.000×g, 4° C. for 1 h. Samples were used fresh or thawed after being stored at −80°.

1.4 Nanoparticle Tracking Analysis

[0095] sEVs were analyzed by nanoparticle tracking analysis (NTA), using the NanoSight LM10 system (NanoSight Ltd., Amesbury, UK), equipped with a 405 nm laser and with the NTA 2.3 analytic software, to define their dimension and profile. All acquisitions were done with Camera level setting at 14 and for each sample, three videos of 30 s duration were recorded. sEVs were diluted (1:1000) in 1 ml vesicle free physiologic solution (Fresenius Kabi, Runcorn, UK). NTA post-acquisition settings were optimized and maintained constant across samples, and each video was then analyzed to measure EV size, distribution and concentration.

1.5 sEVs Angiogenic Assay

[0096] Primary macrovascular endothelial cells (ECs) and microvascular endothelial cells (HMEC) were purchased from Lonza (Basel, Switzerland) and cultured as described by the manufacturer's instructions. The in vitro angiogenesis potency test and the in vivo angiogenesis test were performed as previously described (Cavallari C. et al, “Serum-derived extracellular vesicles (EVs) impact on vascular remodeling and prevent muscle damage in acute hind limb ischemia” (2017) Sci Rep. 7(1):8180). Briefly, 5×10.sup.4 sEVs/target cells were administered throughout the in vitro study. sEVs from single samples were evaluated for their pro-angiogenic activity using BrdU and in vitro tubulogenesis assays. EVs of all the analyzed groups were classified as proangiogenic active or inactive EVs according to a % cut-off value of 50%.

[0097] In vivo angiogenesis was assessed by measuring the growth of blood vessels as previously described (Lopatina T. et al, “Platelet-derived growth factor regulates the secretion of extracellular vesicles by adipose mesenchymal stem cells and enhances their angiogenic potential” (2014) Cell Commun Signal. 12:26). Briefly, ECs (1×10.sup.6 cells/injection) were incubated overnight with sEVs (5×10.sup.10 EVs per 1×10.sup.6 of ECs). Male severe combined immunodeficiency (SCID) mice (6 weeks old) were then injected subcutaneously. An equal number of non-stimulated ECs was used as a negative control. The Matrigel plugs were recovered on day 7 and fixed and stained using the trichrome stain method. The vessel lumen area was determined as previously described (Lopatina T. et al, “Platelet-derived growth factor regulates the secretion of extracellular vesicles by adipose mesenchymal stem cells and enhances their angiogenic potential” (2014) Cell Commun Signal. 12:26).

1.6 TGFβ ELISA Assay

[0098] The TGFβ content in the EVs isolated form serum samples of healthy subjects and patients was measured using a solid phase sandwich Enzyme Linked-Immuno-Sorbent Assay (ELISA, Invitrogen Multispecies TGF-β1 kit, Germany) according to the manufacturer's instructions. Experiments were done in triplicate on samples containing 1×10.sup.11 EVs. The intensity of the colored product obtained in the assay was determined with an ELISA iMark™ Microplate Absorbance Reader (Bio Rad, Switzerland) with absorbance at 450 nm. The concentration of TGFβ present in the EVs preparations was expressed as pg/10.sup.10 EVs.

1.7 miRNA Expression Profiling

[0099] The expression profiles of the miRNAs contained in sEVs (so-called miRNome) was assessed by real-time PCR on 1140 microRNAs using miRNome microRNA Profilers QuantiMir (SBI, System Biosciences), according to the protocol recommended by the manufacturer. The kit includes assays in pre-formatted plates for human microRNAs with three endogenous reference RNA as normalization signals (human U6 snRNA, small nucleolar RNA RNU43 and Hm/Ms/Rt U1 snRNA) on each plate.

[0100] In brief, 100 ng of RNA has been retrotranscribed using High-Capacity cDNA Reverse Transcription Kit (Applied Biosystems, Foster City, Calif., USA). All qRT-PCR reactions were conducted on the StepOnePlus™ Real-Time PCR System under the following conditions: 15′ at 95° C. (PCR Initial activation step) followed by 3-step cycling (15″ at 94° C., 30″ at 55° C., 30″ at 70° C.) for 40 cycles. In the screening, the miRNome was profiled on sEVs collected from serum of healthy subjects, which had been assessed as proangiogenic active (n=3) and proangiogenic inactive (n=3) with the above described potency test. The Ct values for the miRNAs were extrapolated for each sEVs sample analyzed. A Ct representing the average of Cts from different samples (n=3) of both effective and ineffective sEVs populations was normalized against the endogenous reference RNAs and converted in 2.sup.-(ΔCt) values (Livak K J and Schmittgen T D, “Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) method” (2001) Methods 25: 402-408).

[0101] The miRNAs validation was performed on sEVs from healthy donors and patients using the miScript SYBR Green PCR Kit (Qiagen, Valencia, Calif., USA). Briefly, 100 ng of input RNA isolated from sEVs samples were reverse transcribed using the miScript Reverse Transcription Kit and the cDNA thus obtained was used to detect and quantify the miRNAs of interest. Experiments were run in triplicate using 3 ng of cDNA each reaction, as described by the manufacturer's protocol (Qiagen). The following miRNAs were screened in all patient-derived sEVs: miR-126 (SEQ ID NO. 2), miR-21 (SEQ ID NO. 3), miR-296-3p (SEQ ID NO. 4), miR-210 (SEQ ID NO. 5), miR-130a (SEQ ID NO. 1), miR-27a (SEQ ID NO. 6), miR-29a (SEQ ID NO. 7), miR-191 (SEQ ID NO. 8). The amplification data obtained with qRT-PCR were normalized using the RNU6B and the RNU43 reference genes as internal controls. The amplification efficiencies of the target sequence and the endogenous controls were shown to be approximately equal.

1.8 Pathway and Target Prediction Analysis of miRNAs EV Content

[0102] In order to perform EV miRNAs target prediction and biological pathway enrichment analysis, the web-based program DIANAmirPath was used (Collino F. et al, “Exosome and Microvesicle-Enriched Fractions Isolated from Mesenchymal Stem Cells by Gradient Separation Showed Different Molecular Signatures and Functions on Renal Tubular Epithelial Cells” (2017) Stem Cell Rev. (2):226-43). The algorithm microT-CDS was chosen to predict EV-derived miRNA targets using default threshold (microT=0.8). Only biological pathways showing P value<0.01 to all known Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were considered as significantly enriched. Ingenuity IPA pathway analysis was used to predict target genes for miR-130a. The inventors set up the miRNA Target Filter tool on IPA (Qiagen: http://www.qiagen-bioinformatics.com/products/ingenuity-pathway-analysis/) to associate miR-130a with predicted mRNA targets.

1.9 ROC Analysis

[0103] Principal data are presented as means, standard deviations (SD), median and 95% confidence intervals for the two investigated groups “True proangiogenic active sEVs”/“True proangiogenic inactive sEVs”, considered as Reference Standard (RS). In order to evaluate the predictability for miR-130a and TGFβ, the achievement of RS was evaluated using ROC curves (Grund B and Sabin C. “Analysis of biomarker data: logs, odds ratios, and receiver operating characteristic curves” (2010) Curr Opin HIV AIDS 5(6):473-9). The sEVs compositions were classified into the following categories based on the content of miR-130a measured as Ct value and the content of TGFβ, measured as pg/10.sup.10 EVs:

1. sEVs displaying a miR-130a Ct value≥30 were considered proangiogenic ineffective sEVs;
2. sEVs displaying a TGFβ content<23 pg/10.sup.10 EVs were considered proangiogenic ineffective sEVs.

[0104] In order to evaluate the ‘goodness’ of the cut-off score based on ROC curve analysis for the above measures to predict “True proangiogenic inactive sEVs” defined in RS, the predictive capacity was evaluated both for each of the two measures separately and by combining the two measures by a ‘Series’ approach (considering as “proangiogenic ineffective sEVs” the sEVs which are proangiogenic ineffective for both measurements and as “NON proangiogenic ineffective sEVs” the vesicles which are “NON proangiogenic ineffective sEVs” for at least one of the two measures).

[0105] The analysis was based on Sensitivity (Se), Specificity (Sp), and positive Likelihood ratio (LH+) [Probability of identifying as “proangiogenic ineffective sEVs” a “true proangiogenic ineffective sEVs” compared to a “true proangiogenic effective sEVs”] and relative 95% Confidence Intervals values.

1.10 Statistical Analysis

[0106] Data were analyzed using the GraphPad Prism 6.0 Demo program. Results are expressed as mean ±SD or ±SEM, unless otherwise reported. Statistical analysis was carried out using 1-way ANOVA, followed by Tukey's post hoc or multiple comparison, Student t tests for 2-group comparison and Newman-Keuls Multiple Comparison Test where appropriate. The cut-off for statistical significance was set at p<0.05 (*p<0.05, **p<0.01, ***p<0.001).

2. Results

2.1 Characterization of Serum EVs

[0107] In the study conducted by the present inventors, nine samples of sEVs derived from healthy individuals and 36 samples of sEVs derived from patients cohorts were examined for their number and size. The distribution of sEVs size did not show any significant difference among healthy individuals and patients (FIGS. 1A and B). The observed average particle size was around 138 nm. The sEVs number in patients was higher than in healthy subjects (FIG. 1C). Significant higher levels of sEVs were detected in obese and ischemic patients (FIG. 1C).

2.2 Pro-Angiogenic Activity of Serum EVs Derived from Patients

[0108] In order to evaluate in vitro the angiogenic activity of sEVs derived from different patient's groups, a potency test was carried out as described in the Example 1.3 above. The compositions of sEVs showing an average value exceeding 50% were considered as proangiogenic active.

[0109] The results of the angiogenic potency test were validated in vivo using proangiogenic effective and ineffective sEVs from different patient's groups (FIG. 2A-B).

2.3 TGFβ Content in sEVs and Their Angiogenic Potential

[0110] To investigate whether the TGFβ content in sEVs may account for their angiogenic potential, the inventors carried out a ELISA assay on the EVs isolated from serum samples of healthy subjects and patients (diabetic, obese, diabetic/obese and ischemic patients). As shown in FIG. 3, the content of TGFβ measured in the sEVs compositions correlates significantly with the proangiogenic potential of these vesicles in patient cohorts as well as in healthy donors. A cut-off value corresponding to a concentration of TGFβ of 23 pg/10.sup.10 EVs was determined that discriminates proangiogenic effective EVs from ineffective vesicles based on the observation that EVs having a TGFβ content<23 pg/10.sup.10 EVs are more likely to be proangiogenic inactive.

2.4 miRNome Profile of sEVs

[0111] The miRNome analysis carried out by the present inventors on proangiogenic effective and ineffective sEVs from healthy donors (3 samples/each) led to the identification of eight angio-miRNAs as the most differentially expressed, miR-126, miR-21, miR-296-3p, miR-210, miR-130a, miR-27a, miR-29a, miR-191. In particular, miR-126, miR-130a, miR-27a and 296-3p were up-regulated, while miR-21, miR-29a, miR-191 and miR-210 were down-regulated in sEVs with proangiogenic capability.

[0112] To investigate whether the observed difference in miRNAs expression levels in EVs is associated with their functional activity, the inventors carried out a study by comparing the expression of selected miRNAs in sEVs derived from individual healthy donors and patients, with the level of proangiogenic activity of these vesicles as measured with the in vitro potency test. The expression analysis was performed by real time PCR (cut-off Ct value 30). As shown in FIG. 4A, the distribution of the Ct values measured for miR-130a in the EVs from individual subjects (healthy donors and patients) correlates significantly with the results of the angiogenesis potency test performed on these EVs samples. Particularly, it was observed that EVs having a content of miR130a measured as Ct value of Ct>30 have a higher probability of being proangiogenic ineffective.

[0113] Interestingly, the present inventors found also an enrichment of miR-210 in sEVs derived from patients. as previous described in Shalaby S M. et al, “Serum miRNA-499 and miRNA-210: A potential role in early diagnosis of acute coronary syndrome” IUBMB Life. 2016; 68(8):673-82. However, no significant correlation was detected between miR-210 content in sEVs and the proangiogenic activity of these vesicles.

[0114] DIANA mirpath analysis was interrogated using miR-130a by selecting pathways involving at least 15 genes. Again, among others, a significant enrichment of genes involved in the TGFβ pathway was detected (FIG. 4B).

[0115] Network predicted by IPA for miR-130a target genes identified several genes, such as KDR, HOXA5, ROCK1, EPHB6, strongly related to the angiogenic process (FIG. 5A). Moreover, TGFβ and TGFBR1 genes were found among the miR-130a interactors. Overall, the above described results further support the contribution of the TGFβ signaling pathway in sEV-mediated mechanisms of action.

2.5 The Content of miR-130a and TGFβ in sEVs Represents a Valuable Predictive Marker to Identify “True Proangiogenic Ineffective” sEVs.

[0116] The inventors carried out a Receiver Operator Characteristic (ROC) analysis to assess whether the content of miR-130a and TGFβ in sEVs has the predictive capacity to discriminate between sEVs displaying proangiogenic capability and ineffective vesicles. As deduced from the ROC curves illustrated in FIG. 5B, both miR-130a and TGFβ are good predictive measures of “true proangiogenic ineffective sEVs” identified by RS, showing statistically significant AUC values.

[0117] Both measures displayed a good sensitivity to identify as “proangiogenic ineffective” the “true proangiogenic ineffective sEVs” identified by RS. This was particularly evident and further underlined by the LH+=1.88 IC 95% from 1.49 to 2.27, for miR-130a (Se=0.94 IC95% from 0.73 to 0.99) and for TGFβ (Se=0.88 IC95% from 0.66 to 0.97). However, a low specificity value for both measurements was detected (miR-130a: Sp=0.50; TGFβ Sp=0.64).

[0118] By combining the two measures ‘in Series’, i.e. considering as “proangiogenic ineffective” those sEVs defined as “proangiogenic ineffective” in both measures, a good level of sensitivity and an increased specificity value were detected (Sp=0.75; Se=0.82). The LH+ value reported in Table 1 below further supports these results.

TABLE-US-00001 TABLE 1 Test combining miR-130a and TGFβ1 ‘in series’. List of values obtained combining the two measures ‘in Series’ (considering as “proangiogenic ineffective” the sEVs defined as “proangiogenic ineffective” in both miR-130a and TGFβ1 measures). 95% Conf. Int. Parameters Inf Sup Se 0.824 0.59 0.94 Sp 0.750 0.57 0.87 ACC 0.778 0.64 0.87 VPP 0.667 0.45 0.83 VPN 0.875 0.69 0.96 LH+ 3294118 2.62 3.97 LH− 0.235294 −0.81 1.28