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. 2015 Jun 20;6(17):15375-96.
doi: 10.18632/oncotarget.3801.

Proteogenomic Analysis Reveals Exosomes Are More Oncogenic Than Ectosomes

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Free PMC article

Proteogenomic Analysis Reveals Exosomes Are More Oncogenic Than Ectosomes

Shivakumar Keerthikumar et al. Oncotarget. .
Free PMC article

Abstract

Extracellular vesicles (EVs) include the exosomes (30-100 nm) that are produced through the endocytic pathway via the multivesicular bodies and the ectosomes (100-1000 nm) that are released through the budding of the plasma membrane. Despite the differences in the mode of biogenesis and size, reliable markers that can distinguish between exosomes and ectosomes are non-existent. Moreover, the precise functional differences between exosomes and ectosomes remains poorly characterised. Here, using label-free quantitative proteomics, we highlight proteins that could be exploited as markers to discriminate between exosomes and ectosomes. For the first time, a global proteogenomics analysis unveiled the secretion of mutant proteins that are implicated in cancer progression through tumor-derived EVs. Follow up integrated bioinformatics analysis highlighted the enrichment of oncogenic cargo in exosomes and ectosomes. Interestingly, exosomes induced significant cell proliferation and migration in recipient cells compared to ectosomes confirming the oncogenic nature of exosomes. These findings ascertain that cancer cells facilitate oncogenesis by the secretion of mutant and oncoproteins into the tumor microenvironment via exosomes and ectosomes. The integrative proteogenomics approach utilized in this study has the potential to identify disease biomarker candidates which can be later assayed in liquid biopsies obtained from cancer patients.

Keywords: ectosomes; exosomes; extracellular vesicles; integrated OMICs analysis; proteogenomics.

Conflict of interest statement

CONFLICTS OF INTEREST

None declared

Figures

Figure 1
Figure 1. Isolation and characterization of EVs
(a) Western blot analysis (10 μg) of fractions of increasing density obtained by OptiPrepTM density gradient centrifugation (from SH-SY5Y cells) showed the presence of Alix and TSG101 in fractions 3-9 with a clear enrichment in fraction 7 (1.10 g/mL). Pellet obtained from 10,000 g also contained detectable amount of Alix and TSG101. (b) Western blot of GM130 (Golgi marker) and Alix in EVs isolated from 1.10 and 1.14 g/mL densities, 10,000 g pellet and WCL is shown. The absence of GM130 in fractions 7, 9 and 10K confirms the depletion of contaminating vesicles arising from apoptosis. (c) TEM images of EVs isolated from SH-SY5Y cells by OptiPrepTM gradient corresponding to the density 1.10 g/mL showed vesicles in the range of 30-100 nm diameter consistent with exosomes. (d) TEM images of EVs isolated from SH-SY5Y cells by OptiPrepTM gradient corresponding to the density 1.14-1.20 g/mL showed vesicles more than 200 nm in diameter. (e) TEM images of vesicles recovered from 10,000 g pellet (10K) showed aggregates and large EVs secreted by SH-SY5Y neuroblastoma and (f) LIM1215 colorectal cancer cells.
Figure 2
Figure 2. AFM imaging based characterization of EVs isolated by OptiPrep™ density gradient centrifugation
(a) AFM images of fraction 7 (1.10 g/mL) isolated by OptiPrepTM density gradient centrifugation showed vesicles in the range of 30-70 nm. Four profile images (1-4) of the vesicle diameter are also depicted. (b) AFM images of EVs obtained by OptiPrepTM density gradient centrifugation corresponding to 1.14-1.20 g/mL showed enrichment of larger vesicles. Eight profile (1-8) images of the vesicle diameter are also depicted.
Figure 3
Figure 3. Vesicle size distribution and Venn diagram of total and differentially abundant proteins in exosomes, ectosomes and WCL
(a) Pie chart representing the size distribution of vesicles in fraction 7 (1.10 g/mL). (b) Pie chart representing the size distribution of vesicles in fraction 9 (1.14-1.20 g/mL). (c) Label-free quantitative mass spectrometry-based proteomics analysis was performed on WCL, exosomes (1.10 g/mL) and ectosomes (1.14-1.20 g/mL). Venn diagram of differentially expressed proteins in exosomes and (d) ectosomes in comparison to WCL is displayed. (e) Venn diagram of differentially abundant proteins identified in exosomes and ectosomes showed 693 proteins enriched more than 2-fold in exosomes compared to ectosomes. On the contrast, 770 proteins were enriched more than 2-fold ectosomes compared to exosomes.
Figure 4
Figure 4. Fold change of proteins known to be involved in EV biogenesis, trafficking and membrane fusion
Asterisk (*) represents proteins that are not detected in ectosomes and/or 10K while # represents proteins that are not detected in exosomes. Dotted line represents 2-fold cut off. (a) Histogram of proteins involved in ESCRT machinery showed that proteins involved in ESCRT were more than 2-fold abundant (except VPS37D) in exosomes compared to ectosomes. (b) Histogram of tetraspanins shows that exosomes are enriched with tetraspanins CD81, TSPAN14 and TSPAN9 compared to the ectosomes and 10K. CD63, CD9 and TSPAN6 were not enriched more than 2-fold in any data set (even though detected in exosomes with one peptide identification from multiple MS/MS spectra). (c) Among the RAB GTPases, subsets of them were uniquely identified in exosomes (*) while some others in ectosomes and/or 10K (#). The same set of RAB GTPases was uniquely present in both ectosomes and 10K while not detected in exosomes. (d) Proteins known to be involved in trafficking and membrane fusion were all enriched in exosomes except FGA and HLA-A. (e) A literature survey was carried out to identify proteins either involved in ectosome biogenesis or identified in ectosomes. The manually curated protein list was plotted as a histogram based on the proteomics data. Except MMP2, other proteins were not enriched in ectosomes or 10K.
Figure 5
Figure 5. Western blotting analysis of exosomes and ectosomes and polar histogram of top 50 proteins abundant in exosomes and ectosomes
(a) Western blot analysis of exosomes, ectosomes, 10K and WCL. CD81 is exclusively detected in exosomes while MMP2 is unique to ectosomes and 10K. CD63 was enriched in exosomes but was also detected in ectosomes. GM130 is absent in exosomes, ectosomes and 10K confirming the depletion of contaminating vesicles arising from apoptosis. (b) Normalised spectral counts of top 50 abundant proteins are displayed. The list is sorted by the number of times the particular protein is identified in Vesiclepedia. The numbers (outer circle) correspond to the number of studies reported in Vesiclepedia. The color scale represents the protein abundance level in terms of normalised spectral count. Top 50 abundant proteins in exosomes include CD81, flotillins and ADAM10 which are some of the proteins identified more often in Vesiclepedia. (c) Top 50 abundant proteins in ectosomes include GSTP1, RAB(s) and ribosomal proteins.
Figure 6
Figure 6. Functional enrichment analysis of exosomes and ectosomes using FunRich
(a) Biological pathways enriched in proteins that are more than 2-fold abundant in exosomes compared to ectosomes are displayed. Proteins implicated in ESCRT, syndecan signaling and membrane trafficking are enriched in exosomes. When proteins that are more than 2-fold abundant in ectosomes compared to exosomes were analysed using FunRich, proteins implicated in gene expression and translation were enriched in ectosomes. (b) Gene Ontology-based biological processes that are enriched and depleted in proteins differentially abundant in exosomes compared to ectosomes are displayed. (c) Gene Ontology-based molecular functions that are enriched and depleted in proteins differentially abundant in ectosomes compared to exosome are displayed.
Figure 7
Figure 7. Integrated genomics and proteomics workflow
This figure depicts the overview of the proteogenomics analysis involving genomic data from exome sequencing and proteomic data derived from mass-spectrometry. Exosomes and ectosomes were isolated from SH-SY5Y cells and were subjected to label-free quantitative proteomics analysis. Exome sequencing was carried out in SH-SY5Y cells and the SNVs and INDELs detected were used to create a customised mutant protein database. Proteomics analysis was also carried on SH-SY5Y WCL samples. MS/MS spectra from exosomes, ectosomes and WCL samples were searched against the wild-type and mutant database. Two-way Venn diagrams were plotted to depict the overlap of wild-type and mutant proteins identified in two respective samples. A total of 60 and 71 mutant proteins were identified in exosomes and ectosomes, respectively. Venn diagram depicting the wild-type proteins represents all identified proteins and relative abundance is not taken into account.
Figure 8
Figure 8. Proteogenomics landscape of exosomes, ectosomes and WCL of SH-SY5Y cells
This figure depicts the overview of the genomic data derived from exome sequencing and proteomic data obtained by mass spectrometry. (1) The chromosomes represent the human ideogram. (2) Represents histogram density (every 1 Mb size) of single nucleotide variations (SNVs). (3) Represents histogram density (every 1 Mb size) of insertions and deletions (INDELs) identified using exome sequencing data. (4-6) Represents mutant proteins identified by mass spectrometry in exosomes, ectosomes and WCL, respectively, when searched against mutant databases containing the SNVs and INDELS. (7) and (8) represents heat map of differentially expressed proteins in exosomes and ectosomes in comparison to WCL, whereas (9) represents heat map of differentially expressed proteins in exosomes compared to ectosomes.
Figure 9
Figure 9. Oncogenic potential of exosomes and ectosomes
(a) Genes significantly enriched in COSMIC database (p<0.05, chi-square test in different human cancer tissue types (except neuroblastoma which is not significantly enriched-represented by *)) were compared against proteins differentially abundant in exosomes and ectosomes. The normalized spectral counts of the enriched proteins were plotted as a box plot and grouped by the human cancer tissue types. The X-axis represents normalized spectral counts of significantly enriched (p<0.05, chi-square test) proteins whereas Y-axis represents cancer tissue types from COSMIC. Proteins identified in exosomes were implicated in many cancer types as compared to ectosomal proteins. (b) Genes enriched in EST database (NCBI UniGene; p<0.05, chi-square test) were compared against proteins differentially abundant in exosomes and ectosomes. The normalized spectral counts of the proteins were plotted as a box plot and grouped by the tissue types. The Y-axis represents normalized spectral counts of significantly enriched proteins whereas X-axis represents tissue types from EST database. Proteins identified in exosomes were implicated in a wide range of cancer types as compared to ectosomal proteins.
Figure 10
Figure 10. Cell proliferation and migration potential of exosomes and ectosomes
(a) MTS cell proliferation assay was performed with SK-N-BE2 neuroblastoma cells treated with exosomes and ectosomes derived from SH-SY5Y cells for 24 h. As a control, untreated SK-N-BE2 cells were grown. Exosomes induced a 2-fold proliferation of SK-N-BE2 cells. On the contrary, ectosomes did not induce any significant proliferation of SK-N-BE2 cells. Error bars represent standard error of the mean, n=3, * denotes significance (p<0.05). (b) Wound healing assay of neuroblastoma cell line SK-N-BE2 is displayed. Wound was created post reaching 100% confluence, and cells were treated with either exosomes or ectosomes for 24 h. Migration was assessed at 24 h after wounding. Images were taken under the 4x objective of the light microscope. Quantification of wound closure showed that exosomes induced more migration compared to ectosomes. Error bars represent standard error of the mean, n=3, * denotes significance (p<0.05). Student's t-test was used to evaluate statistically significant differences between the values.

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