Hidden reach of the micromanagers

Small interfering RNAs can trigger unintended, microRNA-like off-target effects, but the impact of these effects on functional studies has been controversial. A recent study in BMC Genomics shows that microRNA-like effects can predominate among the 'hits' of functional genomics screens

Dynamical properties of a gene-protein model

A major limitation of the classical random Boolean network model of gene regulatory networks is its synchronous updating, which implies that all the proteins decay at the same rate. Here a model is discussed, where the network is composed of two different sets of nodes, labelled G and P with reference to “genes” and “proteins”. Each gene corresponds to a protein (the one it codes for), while several proteins can simultaneously affect the expression of a gene. Both kinds of nodes take Boolean values. If we look at the genes only, it is like adding some memory terms, so the new state of the gene subnetwork network does no longer depend upon its previous state only. In general, these terms tend to make the dynamics of the network more ordered than that of the corresponding memoryless network. The analysis is focused here mostly on dynamical critical states. It has been shown elsewhere that the usual way of computing the Derrida parameter, starting from purely random initial conditions, can be misleading in strongly non-ergodic systems. So here the effects of perturbations on both genes’ and proteins’ levels is analysed, using both the canonical Derrida procedure and an “extended” one. The results are discussed. Moreover, the stability of attractors is also analysed, measured by counting the fraction of perturbations where the system eventually falls back onto the initial attractor

Interfering with inflammation: a new strategy to block breast cancer self-renewal and progression?

Two recent studies show that epigenetics and inflammation play a relevant role in the regulation of transformation and cancer cell self-renewal in breast tumours, opening up the possibility that cancer progression can be controlled by interfering with inflammation cascades. Struhl's group showed that transient activation of the Src oncoprotein induces transformation and self-renewal of immortal cells via an epigenetic switch involving NF-κB, Lin28, Let-7 microRNA and IL-6. Concomitantly, Wicha's laboratory developed a strategy to selectively target cancer stem cells, retarding tumour growth and reducing metastasis by blocking the IL-8 receptor CXCR1 using either an inhibitor, repertaxin or a specific blocking antibody

MicroRNAs: shortcuts in dealing with molecular complexity?

Recent studies from Clarke's group published in the journal Cell indicate that miRNAs may be the elusive universal stem cell markers that the field of cancer stem cell biology has been seeking. Distinct profiles of miRNAs appear to reflect the state of cell differentiation not only in breast cancer cells, but also in normal mammary epithelial cells. Moreover, they are conserved across tissues and species. The authors of this work also show evidence that downregulation of miRNA-200c in normal and malignant breast stem cells and in embryonal carcinoma cells has functional relevance, being responsible for the proliferative potential of these cells in vitro and in vivo

Kinetic modelling of competition and depletion of shared miRNAs by competing endogenous RNAs

Non-conding RNAs play a key role in the post-transcriptional regulation of mRNA translation and turnover in eukaryotes. miRNAs, in particular, interact with their target RNAs through protein-mediated, sequence-specific binding, giving rise to extended and highly heterogeneous miRNA-RNA interaction networks. Within such networks, competition to bind miRNAs can generate an effective positive coupling between their targets. Competing endogenous RNAs (ceRNAs) can in turn regulate each other through miRNA-mediated crosstalk. Albeit potentially weak, ceRNA interactions can occur both dynamically, affecting e.g. the regulatory clock, and at stationarity, in which case ceRNA networks as a whole can be implicated in the composition of the cell's proteome. Many features of ceRNA interactions, including the conditions under which they become significant, can be unraveled by mathematical and in silico models. We review the understanding of the ceRNA effect obtained within such frameworks, focusing on the methods employed to quantify it, its role in the processing of gene expression noise, and how network topology can determine its reach.Comment: review article, 29 pages, 7 figure

MYC-microRNA-9-metastasis connection in breast cancer

[Excerpt] Metastasis accounts for more than 90% of cancer patients’ mortality. The metastatic process involves multiple steps [1]. Initially, cancer cells from the primary tumor invade adjacent stroma. To acquire this capacity, cells undergo a process called epithelial-mesenchymal transition (EMT), in which cells in re-sponse to signals from the surrounding stroma, undergo a switch between cell phenotypes and acquire mesenchymal properties and show reduced intercel-lular adhesion, allowing cells to be-come motile. Then cells enter systemic circulation, either through the blood or lymph, and finally extravasate into the parenchyma of distant tissues, where they form micrometastasis and prolifer-ate to form secondary tumors [2]. [...

Different level of population differentiation among human genes

<p>Abstract</p> <p>Background</p> <p>During the colonization of the world, after dispersal out of African, modern humans encountered changeable environments and substantial phenotypic variations that involve diverse behaviors, lifestyles and cultures, were generated among the different modern human populations.</p> <p>Results</p> <p>Here, we study the level of population differentiation among different populations of human genes. Intriguingly, genes involved in osteoblast development were identified as being enriched with higher <it>F</it>_STSNPs, a result consistent with the proposed role of the skeletal system in accounting for variation among human populations. Genes involved in the development of hair follicles, where hair is produced, were also found to have higher levels of population differentiation, consistent with hair morphology being a distinctive trait among human populations. Other genes that showed higher levels of population differentiation include those involved in pigmentation, spermatid, nervous system and organ development, and some metabolic pathways, but few involved with the immune system. Disease-related genes demonstrate excessive SNPs with lower levels of population differentiation, probably due to purifying selection. Surprisingly, we find that Mendelian-disease genes appear to have a significant excessive of SNPs with high levels of population differentiation, possibly because the incidence and susceptibility of these diseases show differences among populations. As expected, microRNA regulated genes show lower levels of population differentiation due to purifying selection.</p> <p>Conclusion</p> <p>Our analysis demonstrates different level of population differentiation among human populations for different gene groups.</p

A miRNA-Target Prediction Case Study

Giansanti, V., Castelli, M., Beretta, S., & Merelli, I. (2019). Comparing Deep and Machine Learning Approaches in Bioinformatics: A miRNA-Target Prediction Case Study. In V. V. Krzhizhanovskaya, M. H. Lees, P. M. A. Sloot, J. J. Dongarra, J. M. F. Rodrigues, P. J. S. Cardoso, J. Monteiro, ... R. Lam (Eds.), Computational Science – ICCS 2019: 19th International Conference, Faro, Portugal, June 12–14, 2019, Proceedings, Part III (pp. 31-44). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11538 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-22744-9_3MicroRNAs (miRNAs) are small non-coding RNAs with a key role in the post-transcriptional gene expression regularization, thanks to their ability to link with the target mRNA through the complementary base pairing mechanism. Given their role, it is important to identify their targets and, to this purpose, different tools were proposed to solve this problem. However, their results can be very different, so the community is now moving toward the deployment of integration tools, which should be able to perform better than the single ones. As Machine and Deep Learning algorithms are now in their popular years, we developed different classifiers from both areas to verify their ability to recognize possible miRNA-mRNA interactions and evaluated their performance, showing the potentialities and the limits that those algorithms have in this field. Here, we apply two deep learning classifiers and three different machine learning models to two different miRNA-mRNA datasets, of predictions from 3 different tools: TargetScan, miRanda, and RNAhybrid. Although an experimental validation of the results is needed to better confirm the predictions, deep learning techniques achieved the best performance when the evaluation scores are taken into account.authorsversionpublishe

Using Endogenous MicroRNA Expression Patterns to Visualize Neural Differentiation of Human Pluripotent Stem Cells

Many existing protocols for neuronal differentiation of human pluripotent cells result in heterogeneous cell populations and unsynchronized differentiation, necessitating the development of methods for labeling specific cell populations. Here we describe how microRNA-regulated lentiviral vectors can be used to visualize specific cell populations by exploiting endogenous microRNA expression patterns. This strategy provides a useful tool for visualization and identification of neural progeny derived from human pluripotent stem cells. We provide detailed protocols for lentiviral transduction, neural differentiation, and subsequent analysis of human embryonic stem cells

miR-34a Repression in Proneural Malignant Gliomas Upregulates Expression of Its Target PDGFRA and Promotes Tumorigenesis

Glioblastoma (GBM) and other malignant gliomas are aggressive primary neoplasms of the brain that exhibit notable refractivity to standard treatment regimens. Recent large-scale molecular profiling has revealed distinct disease subclasses within malignant gliomas whose defining genomic features highlight dysregulated molecular networks as potential targets for therapeutic development. The “proneural” designation represents the largest and most heterogeneous of these subclasses, and includes both a large fraction of GBMs along with most of their lower-grade astrocytic and oligodendroglial counterparts. The pathogenesis of proneural gliomas has been repeatedly associated with dysregulated PDGF signaling. Nevertheless, genomic amplification or activating mutations involving the PDGF receptor (PDGFRA) characterize only a subset of proneural GBMs, while the mechanisms driving dysregulated PDGF signaling and downstream oncogenic networks in remaining tumors are unclear. MicroRNAs (miRNAs) are a class of small, noncoding RNAs that regulate gene expression by binding loosely complimentary sequences in target mRNAs. The role of miRNA biology in numerous cancer variants is well established. In an analysis of miRNA involvement in the phenotypic expression and regulation of oncogenic PDGF signaling, we found that miR-34a is downregulated by PDGF pathway activation in vitro. Similarly, analysis of data from the Cancer Genome Atlas (TCGA) revealed that miR-34a expression is significantly lower in proneural gliomas compared to other tumor subtypes. Using primary GBM cells maintained under neurosphere conditions, we then demonstrated that miR-34a specifically affects growth of proneural glioma cells in vitro and in vivo. Further bioinformatic analysis identified PDGFRA as a direct target of miR-34a and this interaction was experimentally validated. Finally, we found that PDGF-driven miR-34a repression is unlikely to operate solely through a p53-dependent mechanism. Taken together, our data support the existence of reciprocal negative feedback regulation involving miR-34 and PDGFRA expression in proneural gliomas and, as such, identify a subtype specific therapeutic potential for miR-34a