Biology subjects

Manzo, M.

Publications and source records attributed to Manzo, M..

3 recordsLinked to original sources

bioRxiv2024

EHZ2 inhibition enhances the activity of platinum chemotherapy in aggressive variant prostate cancer

BackgroundEZH2 promotes aggressive-variant prostate cancer (AVPC) progression via histone H3-Lysine-27 tri-methylation (H3K27me3). We hypothesize that epigenetic reprogramming via EZH2 inhibitors (EZH2i) improves the efficacy of chemotherapy in AVPC. MethodsWe studied the expression of EZH2 in clinical prostate cancer cohorts (bioinformatics). We determined the effect of EZH2i on both cellular- and cell-free-H3K27me3 levels. We measured effects of carboplatin with/without EZH2i on AVPC cell viability (IC50). We studied how EZH2i modulate gene expression (RNA Seq). ResultsEZH2 was significantly up-regulated in AVPC vs other prostate cancer types. EZH2i reduced both cellular and cell free-H3K27me3 levels. EZH2i significantly reduced carboplatin IC50. EZH2i reduced the expression of DNA repair and increased the expression of pro-apoptotic genes. Article HighlightsO_LIPolycomb-mediated gene silencing promotes prostate cancer progression C_LIO_LIAggressive-variant prostate cancers (AVPCs) are characterized by increased activity of the Polycomb-Repressive Complex 2 (PRC2) C_LIO_LIHere we show that PRC2 inhibitors are scarcely effective as monotherapy in ACPC cells C_LIO_LIHowever the combination of PRC2 inhibitors and carboplatin is highly synergistic C_LIO_LIRNA Seq studies revealed that PRC2 inhibitors enhance carboplatin activity by modulating several key pathways, including DNA repair and apoptosis. C_LI

pharmacology and toxicology↗

bioRxiv2024

HELP: A computational framework for labelling and predicting human context-specific essential genes

Machine learning-based approaches are particularly suitable for identifying essential genes as they allow the generation of predictive models trained on features from multi-source data. Gene essentiality is neither binary nor static but determined by the context. The databases for essential gene annotation do not permit the personalisation of the context, and their update can be slower than the publication of new experimental data. We propose HELP (Human Gene Essentiality Labelling & Prediction), a computational framework for labelling and predicting essential genes. Its double scope allows for identifying genes based on dependency or not on experimental data. The effectiveness of the labelling method was demonstrated by comparing it with other approaches in overlapping the reference sets of essential gene annotations, where HELP demonstrated the best compromise between false and true positive rates. The gene attributes, including multi-omics and network embedding features, lead to high-performance prediction of essential genes while confirming the existence of essentiality nuances. Author summaryEssential genes (EGs) are commonly defined as those required for an organism or cells growth and survival. The essentiality is strictly dependent on both environmental and genetic conditions, determining a difference between those considered common EGs (cEGs), essential in most of the contexts considered, and those essential specifically to one or few contexts (context-specific EGs, csEGs). In this paper, we present a library of tools and methodologies to address the identification and prediction of cEGs and csEGs. Furthermore, we attempt to experimentally explore the statement that essentiality is not a binary property by identifying, predicting and analysing an intermediate class between the Essential (E) and Not Essential (NE) genes. Among the multi-source data used to predict the EGs, we found the best attributes combination to capture the essentiality. We demonstrated that the additional class of genes we defined as "almost Essential" shows differences in these attributes from the E and NE genes. We believe that investigating the context-specificity and the dynamism of essentiality is particularly relevant to unravelling crucial insights into biological mechanisms and suggesting new candidates for precision medicine.

bioinformatics↗

bioRxiv2020

Flanking sequence preference modulates de novo DNA methylation in the mouse genome

Mammalian de novo DNA methyltransferases (DNMT) are responsible for the establishment of cell-type-specific DNA methylation in healthy and diseased tissues. Through genome-wide analysis of de novo methylation activity in murine stem cells we uncover that DNMT3A prefers to methylate CpGs followed by cytosines or thymines, while DNMT3B predominantly methylates CpGs followed by guanines or adenines. These signatures are further observed at non-CpG sites, resembling methylation context observed in specialised cell types, including neurons and oocytes. We further show that these preferences are not mediated by the differential recruitment of the two de novo DNMTs to the genome but are resulting from structural differences in their catalytic domains. Molecular dynamics simulations suggest that, in case of DNMT3A, the preference is due to favourable polar interactions between the flexible Arg836 side chain and the guanine that base-pairs with the cytosine following the CpG. This context-dependent de novo DNA methylation provides additional insights into the complex regulation of methylation patterns in different cell types.

molecular biology↗