An inverted Caveolin-1 topology defines a novel exosome secreted from prostate cancer cells

ABSTRACTCaveolin-1 (Cav1) expression and secretion is associated with prostate cancer (PCa) disease progression but the mechanisms underpinning Cav1 release remain poorly understood. Numerous studies have shown Cav1 can be secreted within exosome-like vesicles, but antibody-mediated neutralization can mitigate PCa progression; this is suggestive of an inverted (non-exosomal) Cav1 topology. Here we show that Cav1 can be secreted from specific PCa types in an inverted vesicle-associated form consistent with the features of bioactive Cav1 secretion. Characterization of the isolated vesicles by electron microscopy, single molecule fluorescent microscopy and proteomics reveals they represent a novel class of exosomes ∼40 nm in diameter containing ∼50-60 copies of Cav1 and strikingly, are released via a non-canonical secretory autophagy pathway. This study provides novel insights into a mechanism whereby Cav1 translocates from a normal plasma membrane distribution to an inverted secreted form implicated in PCa disease progression.

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Identification of caveolin-1 domain signatures via machine learning and graphlet analysis of single-molecule super-resolution data

Bioinformatics ◽

10.1093/bioinformatics/btz113 ◽

2019 ◽

Vol 35 (18) ◽

pp. 3468-3475 ◽

Cited By ~ 4

Author(s):

Ismail M Khater ◽

Fanrui Meng ◽

Ivan Robert Nabi ◽

Ghassan Hamarneh

Keyword(s):

Prostate Cancer ◽

Machine Learning ◽

Cancer Cells ◽

Single Molecule ◽

Classification Accuracy ◽

Supplementary Information ◽

Prostate Cancer Cells ◽

Localization Microscopy ◽

Caveolin 1 ◽

Single Molecule Localization Microscopy

Abstract Motivation Network analysis and unsupervised machine learning processing of single-molecule localization microscopy of caveolin-1 (Cav1) antibody labeling of prostate cancer cells identified biosignatures and structures for caveolae and three distinct non-caveolar scaffolds (S1A, S1B and S2). To obtain further insight into low-level molecular interactions within these different structural domains, we now introduce graphlet decomposition over a range of proximity thresholds and show that frequency of different subgraph (k = 4 nodes) patterns for machine learning approaches (classification, identification, automatic labeling, etc.) effectively distinguishes caveolae and scaffold blobs. Results Caveolae formation requires both Cav1 and the adaptor protein CAVIN1 (also called PTRF). As a supervised learning approach, we applied a wide-field CAVIN1/PTRF mask to CAVIN1/PTRF-transfected PC3 prostate cancer cells and used the random forest classifier to classify blobs based on graphlet frequency distribution (GFD). GFD of CAVIN1/PTRF-positive (PTRF+) and -negative Cav1 clusters showed poor classification accuracy that was significantly improved by stratifying the PTRF+ clusters by either number of localizations or volume. Low classification accuracy (<50%) of large PTRF+ clusters and caveolae blobs identified by unsupervised learning suggests that their GFD is specific to caveolae. High classification accuracy for small PTRF+ clusters and caveolae blobs argues that CAVIN1/PTRF associates not only with caveolae but also non-caveolar scaffolds. At low proximity thresholds (50–100 nm), the caveolae groups showed reduced frequency of highly connected graphlets and increased frequency of completely disconnected graphlets. GFD analysis of single-molecule localization microscopy Cav1 clusters defines changes in structural organization in caveolae and scaffolds independent of association with CAVIN1/PTRF. Supplementary information Supplementary data are available at Bioinformatics online.

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