Biology subjects

Mahlab-Aviv, S.

Publications and source records attributed to Mahlab-Aviv, S..

3 recordsLinked to original sources

bioRxiv2020

Spliceosome-Associated MicroRNAs Identified in Breast Cancer Cells Act on Nuclear Targets and Are Potential Indicators for Tumorigenicity

MicroRNAs (miRNAs) act as negative regulators of gene expression in the cytoplasm. Previous studies identified miRNAs associated with the spliceosome. Here we study three breast-derived cell-lines with increased tumorigenicity (from MCF-10A to MCF-7 and MDA-MB-231) and compared their miRNA sequences at the spliceosome fraction (SF). We report that the SF-miRNAs expression, identity, and pre-miRNA segmental composition vary across these cell-lines. The expression of the majority of the abundant SF-miRNAs (e.g. miR-100, miR-30a, and let-7 members) shows an opposite trend in view of the literature and breast cancer large cohorts. The results suggest that SF-miRNAs act in the nucleus on alternative targets than in the cytoplasm. One such miRNA is miR-7704 whose genomic position overlaps HAGLR, a cancer-related lncRNA. We found an inverse expression of miR-7704 and HAGLR in the tested cell lines. Moreover, inhibition of miR-7704 caused an increase in HAGLR expression. Furthermore, increasing miR-7704 levels attenuated the MDA-MB-231 cell-division rate. While miR-7704 acts as oncomiR in breast cancer patients, it has a tumor-suppressing function in SF, with HAGLR being its nuclear target. Manipulating miR-7704 levels is a potential lead for altering tumorigenicity. Altogether, we report on the potential of manipulating SF-miRNAs as an unexplored route for breast cancer therapeutics.

cancer biology↗

bioRxiv2020

Sensitivity of Gene Sets to miRNA Regulation: A Cell-Based Probabilistic Approach

Mature microRNAs (miRNAs) are small, non-coding RNA molecules that function by base-pairing with mRNAs. In multicellular organisms, miRNAs lead to mRNA destabilization and translation arrest. Importantly, the quantities and stichometry of miRNAs/mRNAs determine the miRNA regulation characteristics of specific cells. In this study, we used COMICS (Competition of miRNA Interactions in Cell Systems), a stochastic computational iterative framework to characterize genes by their sensitivity and robustness to miRNA regulation. We monitor the cell state by quantifying the retention level for all mRNAs, at the end of 100,000 simulation iterations. In HeLa cells, we partitioned all genes to five classes according to their decay rates. We show that the largest class (69% of genes) is apparently resistant to miRNA regulation. We created in silico perturbations using overexpressing of all major miRNAs (248 types) at various levels relative to the basal level (x1 to x1000). We further classified genes according to the differential behaviour for any pair of conditions of miRNA expression profile. Based on such measure (OvereXpression Ratio, OXR), we identified a small number of gene sets that are especially sensitive to OXR. Our results expose an overlooked quantitative dimension for set of genes and miRNA regulation in living cells.

bioinformatics↗

bioRxiv2018

The Translation Machinery Is Immune from miRNA Perturbations: A Cell-Based Probabilistic Approach

Mature microRNAs (miRNAs) regulate most human genes through direct base-pairing with mRNAs. We investigate some underlying principles of such regulation. To this end, we overexpressed miRNAs in different cell types and measured the mRNA decay rate under transcriptional arrest. Parameters extracted from these experiments were incorporated into a computational stochastic framework which was developed to simulate the cooperative action of miRNAs in living cells. We identified gene sets that exhibit coordinated behavior with respect to all major miRNAs, over a broad range of overexpression levels. While a small set of genes is highly sensitive to miRNA manipulations, about 180 genes are insensitive to miRNA manipulations as measured by their degree of mRNA retention. The insensitive genes are associated with the translation machinery. We conclude that the stochastic nature of miRNAs reveals an unexpected robustness of gene expression in living cells. Moreover, the use of a systematic probabilistic approach exposes design principles of cells states and in particular, the translational machinery.\n\nHighlightsO_LIA probabilistic-based simulator assesses the cellular response to thousands of miRNA overexpression manipulations\nC_LIO_LIThe translational machinery displays an exceptional resistance to manipulations of miRNAs.\nC_LIO_LIThe insensitivity of the translation machinery to miRNA manipulations is shared by different cell types\nC_LIO_LIThe composition of the most abundant miRNAs dominates cell identity\nC_LI

systems biology↗