Dataset Information

Deep Sequencing Analysis Reveals Distinctive Non-Coding RNAs When Comparing Tumor Multidrug-Resistant Cells and Extracellular Vesicles with Drug-Sensitive Counterparts.

ABSTRACT: Multidrug resistance (MDR) is one of the main limitations of cancer treatment. The overexpression of drug-efflux pumps, such as P-glycoprotein (P-gp), is a major cause of MDR. Importantly, different studies have shown that extracellular vesicles (EVs) participate in the communication between MDR cells and drug-sensitive counterparts, promoting dissemination of the MDR phenotype. In the present work, we aimed to identify RNA species present in MDR cells and in EVs released by those cells, which may be associated with the MDR phenotype. The RNA content from two pairs (leukemia and lung cancer) of MDR (P-gp overexpressing) cells and their drug-sensitive counterparts, as well as from their EVs, was analyzed by deep sequencing. Our results showed distinctive transcripts for MDR cells and their EVs, when compared with their drug-sensitive counterparts. Remarkably, two pseudogenes (a novel pseudogene and RNA 5.8S ribosomal pseudogene 2) were found to be increased in EVs released by MDR cells in both leukemia and lung cancer models. Moreover, six miRs (miR-204-5p, miR-139-5p, miR-29c-5p, miR-551b-3p, miR-29b-2-5p, and miR-204-3p) exhibited altered levels in lung cancer MDR cells and their EVs. This study provides insights into the contribution of EVs to MDR.

SUBMITTER: Sousa D

PROVIDER: S-EPMC7016831 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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Deep Sequencing Analysis Reveals Distinctive Non-Coding RNAs When Comparing Tumor Multidrug-Resistant Cells and Extracellular Vesicles with Drug-Sensitive Counterparts.

Sousa Diana D Matthiesen Rune R Lima Raquel T RT Vasconcelos M Helena MH

Cancers 20200114 1

Multidrug resistance (MDR) is one of the main limitations of cancer treatment. The overexpression of drug-efflux pumps, such as P-glycoprotein (P-gp), is a major cause of MDR. Importantly, different studies have shown that extracellular vesicles (EVs) participate in the communication between MDR cells and drug-sensitive counterparts, promoting dissemination of the MDR phenotype. In the present work, we aimed to identify RNA species present in MDR cells and in EVs released by those cells, which m ...[more]

PMID: 31947507

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Project description:BackgroundAmyotrophic lateral sclerosis (ALS) is a multifactorial, multisystem motor neuron disease for which currently there is no effective treatment. There is an urgent need to identify biomarkers to tackle the disease's complexity and help in early diagnosis, prognosis, and therapy. Extracellular vesicles (EVs) are nanostructures released by any cell type into body fluids. Their biophysical and biochemical characteristics vary with the parent cell's physiological and pathological state and make them an attractive source of multidimensional data for patient classification and stratification.MethodsWe analyzed plasma-derived EVs of ALS patients (n = 106) and controls (n = 96), and SOD1G93A and TDP-43Q331K mouse models of ALS. We purified plasma EVs by nickel-based isolation, characterized their EV size distribution and morphology respectively by nanotracking analysis and transmission electron microscopy, and analyzed EV markers and protein cargos by Western blot and proteomics. We used machine learning techniques to predict diagnosis and prognosis.ResultsOur procedure resulted in high-yield isolation of intact and polydisperse plasma EVs, with minimal lipoprotein contamination. EVs in the plasma of ALS patients and the two mouse models of ALS had a distinctive size distribution and lower HSP90 levels compared to the controls. In terms of disease progression, the levels of cyclophilin A with the EV size distribution distinguished fast and slow disease progressors, a possibly new means for patient stratification. Immuno-electron microscopy also suggested that phosphorylated TDP-43 is not an intravesicular cargo of plasma-derived EVs.ConclusionsOur analysis unmasked features in plasma EVs of ALS patients with potential straightforward clinical application. We conceived an innovative mathematical model based on machine learning which, by integrating EV size distribution data with protein cargoes, gave very high prediction rates for disease diagnosis and prognosis.

Project description:Multidrug resistance (MDR) is a major impediment to curative cancer chemotherapy. The ATP-Binding Cassette transporters ABCG2, ABCB1 and ABCC2 form a unique defense network against multiple structurally and functionally distinct chemotherapeutics, thereby resulting in MDR. Thus, deciphering novel mechanisms of MDR and their overcoming is a major goal of cancer research. Recently we have shown that overexpression of ABCG2 in the membrane of novel extracellular vesicles (EVs) in breast cancer cells results in mitoxantrone resistance due to its dramatic sequestration in EVs. However, nothing is known about EVs structure, biogenesis and their ability to concentrate multiple antitumor agents. To this end, we here found that EVs are structural and functional homologues of bile canaliculi, are apically localized, sealed structures reinforced by an actin-based cytoskeleton and secluded from the extracellular milieu by the tight junction proteins occludin and ZO-1. Apart from ABCG2, ABCB1 and ABCC2 were also selectively targeted to the membrane of EVs. Moreover, Ezrin-Radixin-Moesin protein complex selectively localized to the border of the EVs membrane, suggesting a key role for the tethering of MDR pumps to the actin cytoskeleton. The ability of EVs to concentrate and sequester different antitumor drugs was also explored. Taking advantage of the endogenous fluorescence of anticancer drugs, we found that EVs-forming breast cancer cells display high level resistance to topotecan, imidazoacridinones and methotrexate via efficient intravesicular drug concentration hence sequestering them away from their cellular targets. Thus, we identified a new modality of anticancer drug compartmentalization and resistance in which multiple chemotherapeutics are actively pumped from the cytoplasm and highly concentrated within the lumen of EVs via a network of MDR transporters differentially targeted to the EVs membrane. We propose a composite model for the structure and function of MDR pump-rich EVs in cancer cells and their ability to confer multiple anticancer drug resistance.

Dataset Information

Deep Sequencing Analysis Reveals Distinctive Non-Coding RNAs When Comparing Tumor Multidrug-Resistant Cells and Extracellular Vesicles with Drug-Sensitive Counterparts.

Publications

Deep Sequencing Analysis Reveals Distinctive Non-Coding RNAs When Comparing Tumor Multidrug-Resistant Cells and Extracellular Vesicles with Drug-Sensitive Counterparts.

Similar Datasets

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