ABSTRACT
BACKGROUND: Nucleosomes wrap the DNA into the nucleus of the Eukaryote cell and regulate its transcription phase. Several studies indicate that nucleosomes are determined by the combined effects of several factors, including DNA sequence organization. Interestingly, the identification of nucleosomes on a genomic scale has been successfully performed by computational methods using DNA sequence as input data. RESULTS: In this work, we propose CORENup, a deep learning model for nucleosome identification. CORENup processes a DNA sequence as input using one-hot representation and combines in a parallel fashion a fully convolutional neural network and a recurrent layer. These two parallel levels are devoted to catching both non periodic and periodic DNA string features. A dense layer is devoted to their combination to give a final classification. CONCLUSIONS: Results computed on public data sets of different organisms show that CORENup is a state of the art methodology for nucleosome positioning identification based on a Deep Neural Network architecture. The comparisons have been carried out using two groups of datasets, currently adopted by the best performing methods, and CORENup has shown top performance both in terms of classification metrics and elapsed computation time.
Subject(s)
Genomics/methods , Neural Networks, Computer , Nucleosomes/metabolism , HumansABSTRACT
Osteolytic bone disease is the major complication associated with the progression of multiple myeloma (MM). Recently, extracellular vesicles (EVs) have emerged as mediators of MM-associated bone disease by inhibiting the osteogenic differentiation of human mesenchymal stem cells (hMSCs). Here, we investigated a correlation between the EV-mediated osteogenic inhibition and MM vesicle content, focusing on miRNAs. By the use of a MicroRNA Card, we identified a pool of miRNAs, highly expressed in EVs, from MM cell line (MM1.S EVs), expression of which was confirmed in EVs from bone marrow (BM) plasma of patients affected by smoldering myeloma (SMM) and MM. Notably,we found that miR-129-5p, which targets different osteoblast (OBs) differentiation markers, is enriched in MM-EVs compared to SMM-EVs, thus suggesting a selective packaging correlated with pathological grade. We found that miR-129-5p can be transported to hMSCs by MM-EVs and, by the use of miRNA mimics, we investigated its role in recipient cells. Our data demonstrated that the increase of miR-129-5p levels in hMSCs under osteoblastic differentiation stimuli inhibited the expression of the transcription factor Sp1, previously described as a positive modulator of osteoblastic differentiation, and of its target the Alkaline phosphatase (ALPL), thus identifying miR-129-5p among the players of vesicle-mediated bone disease.
ABSTRACT
Phosphatase PTP1B has become a therapeutic target for the treatment of type 2-diabetes, whereas recent studies have revealed that PTP1B plays a pivotal role in pathophysiology and development of breast cancer. Oleuropein is a natural, phenolic compound with anticancer activity. The aim of this study was to address the question whether PTP1B constitutes a target for oleuropein in breast cancer MCF-7 cells. The cellular MCF-7 breast cancer model was used in the study. The experiments were performed using cellular viability tests, Elisa assays, immunoprecipitation, flow cytometry analyses and computer modelling. Herein, we evidenced that the reduced activity of phosphatase PTP1B after treatment with oleuropein is strictly correlated with decreased MCF-7 cellular viability and cell cycle arrest. These results provide new insight into further research on oleuropein and possible role of the compound in adjuvant treatment of breast cancer.
