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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.

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:In recent years, EVs have emerged as promising vehicles for coding and non-coding RNAs (ncRNAs), which have demonstrated remarkable potential as biomarkers for various diseases, including chronic liver diseases (CLDs). EVs are small, membrane-bound particles released by cells, carrying an arsenal of ncRNAs, including microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and other ncRNA species, such as piRNAs, circRNAs, and tsRNAs. These ncRNAs act as key regulators of gene expression, splicing, and translation, providing a comprehensive molecular snapshot of the cells of origin. The non-invasive nature of EV sampling, typically via blood or serum collection, makes them highly attractive candidates for clinical biomarker applications. Moreover, EV-encapsulated ncRNAs offer unique advantages over traditional cell-free ncRNAs due to their enhanced stability within the EVs, hence allowing for their detection in circulation for extended periods and enabling more sensitive and reliable biomarker measurements. Numerous studies have investigated the potential of EV-enclosed ncRNAs as biomarkers for CLD. MiRNAs, in particular, have gained significant attention due to their ability to rapidly respond to changes in cellular stress and inflammation, hallmarks of CLD pathogenesis. Elevated levels of specific miRNAs have been consistently associated with various CLD subtypes, including metabolic dysfunction-associated steatotic liver disease (MASLD), metabolic dysfunction-associated steatohepatitis (MASH), and chronic hepatitis B and C. LncRNAs have also emerged as promising biomarkers for CLD. These transcripts are involved in a wide range of cellular processes, including liver regeneration, fibrosis, and cancer progression. Studies have shown that lncRNA expression profiles can distinguish between different CLD subtypes, providing valuable insights into disease progression and therapeutic response. Promising EV-enclosed ncRNA biomarkers for CLD included miR-122 (elevated levels of miR-122 are associated with MASLD progression and liver fibrosis), miR-21 (increased expression of miR-21 is linked to liver inflammation and fibrosis in CLD patients), miR-192 (elevated levels of miR-192 are associated with more advanced stages of CLD, including cirrhosis and HCC), LncRNA HOTAIR (increased HOTAIR expression is associated with MASLD progression and MASH development), and LncRNA H19 (dysregulation of H19 expression is linked to liver fibrosis and HCC progression). In the present review, we focus on the EV-enclosed ncRNAs as promising tools for the diagnosis and monitoring of CLD of various etiologies.

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:Colorectal cancer (CRC) is the second most prevalent cancer type worldwide, which highlights the urgent need for non-invasive biomarkers for its early detection and improved prognosis. We aimed to investigate the patterns of long non-coding RNAs (lncRNAs) in small extracellular vesicles (sEVs) collected from low-volume blood serum specimens of CRC patients, focusing on their potential as diagnostic biomarkers. Our research comprised two phases: an initial exploratory phase involving RNA sequencing of sEVs from 76 CRC patients and 29 healthy controls, and a subsequent validation phase with a larger cohort of 159 CRC patients and 138 healthy controls. Techniques such as dynamic light scattering, transmission electron microscopy, and Western blotting were utilized for sEV characterization. Optimized protocol for sEV purification, RNA isolation and preamplification was applied to successfully sequence the RNA content of sEVs and validate the results by RT-qPCR. We successfully isolated sEVs from blood serum and prepared sequencing libraries from a low amount of RNA. High-throughput sequencing identified differential levels of 460 transcripts between CRC patients and healthy controls, including mRNAs, lncRNAs, and pseudogenes, with approximately 20% being lncRNAs, highlighting several tumor-specific lncRNAs that have not been associated with CRC development and progression. The validation phase confirmed the upregulation of three lncRNAs (NALT1, AL096828, and LINC01637) in blood serum of CRC patients. This study not only identified lncRNA profiles in a population of sEVs from low-volume blood serum specimens of CRC patients but also highlights the value of innovative techniques in biomolecular research, particularly for the detection and analysis of low-abundance biomolecules in clinical samples. The identification of specific lncRNAs associated with CRC provides a foundation for future research into their functional roles in cancer development and potential clinical applications.

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.

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