Biology subjects

Monteleone, E.

Publications and source records attributed to Monteleone, E..

5 recordsLinked to original sources

bioRxiv2022

Chromatin organization drives the exploration strategy of nuclear factors

Nuclear Factors (NFs) rapidly scan the genome for their targets, but the role of nuclear organization in such search is uncharted. Here we analyzed how multiple NFs explore chromatin, by combining live-cell single-molecule tracking with multifocal structured illumination of DNA density. We find that NFs displaying higher bound fractions sample DNA dense regions more exhaustively. Focusing on the tumor-suppressor p53, we demonstrated that this NF search for its targets by alternating between rapid diffusion in the interchromatin compartment and compact sampling of chromatin dense regions. Efficient p53 targeting requires balanced IDR/chromatin interactions: adding an exogenous IDR potentiates p53-mediated target gene activation, but excessive IDR/IDR interactions lead to p53 condensates, derailing its search and downregulating transcription. Our findings highlight the role of NF IDRs on their search and showcase a powerful method to generate traffic maps of the eukaryotic nucleus and dissect how nuclear organization guides NFs action.

biophysics↗

bioRxiv2021

Small transcriptional differences lead to distinct NF-κB dynamics in quasi-identical cells

Transcription factor dynamics is fundamental to determine the activation of accurate transcriptional programs and yet is heterogeneous at single-cell level, even between very similar cells. We asked how such heterogeneity emerges for the nuclear factor {kappa}B (NF-{kappa}B), whose dynamics have been reported to cover a wide spectrum of behaviors, including persistent, oscillatory and weak activation. We found that clonal populations of immortalized fibroblasts derived from a single mouse embryo display robustly distinct dynamics upon tumor necrosis factor (TNF-) stimulation, which give rise to differences in the transcription of NF-{kappa}B targets. Notably, standard transcriptomic analyses indicate that the clones differ mostly in transcriptional programs related with development, but not in TNF- signaling. However, by combining transcriptomics data and simulations we show how the expression levels of genes coding for proteins of the signaling cascade determine the differences in early NF-{kappa}B activation; differences in the expression of I{kappa}B determine differences in its persistence and oscillatory behavior. The same analysis predicts inter-clonal differences in the NF-{kappa}B response to IL-1{beta}. We propose that small (less than twofold) differences at transcript level can lead to distinct transcription factor dynamics in cells within homogeneous cell populations, and all the more so among different cell types.

systems biology↗

bioRxiv2021

S-phase induced RNAs initiate formation of DNA replication origins

Coordinated initiation of DNA replication is essential to ensure efficient and timely DNA synthesis. Yet, the mechanism governing the "initiation" process in eukaryotic cells remains elusive. Here, we present data demonstrating a novel feature of RNAs transcribed in the proximity of actively replicating gene loci. We show that S-phAse-RNAs aNChoring ORC1 (ANCORs) to the histone variant H2A.Z are licensors of the DNA replication process. The concomitant ANCOR-H2A.Z interaction is essential for the cells to initiate duplication of their genetic heritage. Widespread and locus-specific perturbations of these transcripts correlate with anomalous replication patterns and loss of the replicative marker at the origin site. Collectively, we unveil an RNA-mediated mechanism as the missing link for the generation of active replication origins and delineate a potential strategy to modulate replication in human cells at a local and global level.

molecular biology↗

bioRxiv2019

Network analysis allows to unravel breast cancer molecular features and to identify novel targets

The behaviour of complex biological systems is determined by the orchestrated activity of many components interacting with each other, and can be investigated by networks. In particular, gene co-expression networks have been widely used in the past years thanks to the increasing availability of huge gene expression databases. Breast cancer is a heterogeneous disease usually classified either according to immunohistochemical features or by expression profiling, which identifies the 5 subtypes luminal A, luminal B, basal-like, HER2-positive and normal-like. Basal-like tumours are the most aggressive subtype, for which so far no targeted therapy is available.\n\nMaking use of the WGCNA clustering method to reconstruct breast cancer transcriptional networks from the METABRIC breast cancer dataset, we developed a platform to address specific questions related to breast cancer biology. In particular, we obtained gene modules significantly correlated with survival and age of onset, useful to understand how molecular features and gene expression patterns are organized in breast cancer. We next generated subtype-specific gene networks and in particular identified two modules that are significantly more connected in basal-like breast cancer with respect to all other subtypes, suggesting relevant biological functions. We demonstrate that network centrality (kWithin) is a suitable measure to identify relevant genes, since we could show that it correlates with clinical features and that it provides a mean to select potential upstream regulators of a module with high reliability. Finally, we showed the feasibility of adding meaning to the networks by combining them with independently obtained data related to activated pathways.\n\nIn conclusion, our platform allows to identify groups of genes highly relevant in breast cancer and possibly amenable to drug targeting, due to their ability to regulate survival-related gene networks. This approach could be successfully extended to other BC subtypes, and to all tumor types for which enough expression data are available.

cancer biology↗

bioRxiv2018

SP1 and STAT3 functionally synergize to induce the RhoU small GTPase and a subclass of non-canonical WNT responsive genes correlating with poor prognosis in breast cancer.

Breast cancer is a complex disease in which heterogeneity makes clinical management very challenging. Although breast cancer subtypes classified according to specific molecular features are associated to better or worse prognosis, the identification of specific markers predicting disease outcome within the single subtypes still lags behind. Both the non-canonical WNT and the STAT3 pathways are often constitutively activated in breast tumors, and both can induce the small GTPase RhoU gene transcription. Here we show that RhoU transcription can be triggered by both canonical and non-canonical WNT ligands via the activation of JNK and the recruitment of the SP1 transcription factor to the RhoU promoter, identifying for the first time SP1 as a JNK-dependent mediator of WNT signaling. RhoU down-regulation by silencing or treatment with JNK, SP1 or STAT3 inhibitors lead to impaired cell migration in basal-like MDA-MB-231 cells, which display constitutive activation of both the non-canonical WNT and STAT3 pathways. These data suggest that STAT3 and SP1 can cooperate to induce high RhoU expression and enhance migration of breast cancer cells. In vivo binding of both factors characterizes a group of SP1/STAT3 responsive genes belonging to the non-canonical WNT and the IL-6/STAT3 pathways. High expression of this signature is significantly correlated with poor prognosis across all profiled patients. Thus, concomitant binding of both STAT3 and SP1 defines a subclass of genes contributing to breast cancer aggressiveness, suggesting the relevance of developing novel targeted therapies combining inhibitors of the STAT3 and WNT pathways or of their downstream mediators.\n\nNovelty and ImpactThe WNT and STAT3 pathways are often activated in breast tumors, but whether they can cooperate towards aggressiveness is not presently known. Here the authors show that WNT ligands can elicit the activation of the transcription factor SP1, which cooperates with STAT3 to induce a subset of non-canonical WNT and IL-6/STAT3 genes. Expression of this gene signature correlates with bad prognosis in breast cancer, suggesting coordinated interference with both TFs as a novel therapeutic option.

cancer biology↗