Proneural-Mesenchymal Transition: Phenotypic Plasticity to Acquire Multitherapy Resistance in Glioblastoma

Glioblastoma (GBM) is an extremely aggressive tumor of the central nervous system, with a prognosis of 12\u201315 months and just 3\u20135% of survival over 5 years. This is mainly because most patients suffer recurrence after treatment that currently consists in maximal resection followed by radio- and chemotherapy with temozolomide. The recurrent tumor shows a more aggressive behavior due to a phenotypic shift toward the mesenchymal subtype. Proneural-mesenchymal transition (PMT) may represent for GBM the equivalent of epithelial\u2013mesenchymal transition associated with other aggressive cancers. In this review we frame this process in the high degree of phenotypic inter- and intra-tumor heterogeneity of GBM, which exists in different subtypes, each one characterized by further phenotypic variability in its stem-cell compartment. Under the selective pressure of different treatment agents PMT is induced. The mechanisms involved, as well as the significance of such event in the acquisition of a multitherapy resistance phenotype, are taken in consideration for future perspectives in new anti-GBM therapeutic options

HMGA1 Modulates Gene Transcription Sustaining a Tumor Signalling Pathway Acting on the Epigenetic Status of Triple-Negative Breast Cancer Cells

Chromatin accessibility plays a critical factor in regulating gene expression in cancer cells. Several factors, including the High Mobility Group A (HMGA) family members, are known to participate directly in chromatin relaxation and transcriptional activation. The HMGA1 oncogene encodes an architectural chromatin transcription factor that alters DNA structure and interacts with transcription factors favouring their landing onto transcription regulatory sequences. Here, we provide evidence of an additional mechanism exploited by HMGA1 to modulate transcription. We demonstrate that, in a triple-negative breast cancer cellular model, HMGA1 sustains the action of epigenetic modifiers and in particular it positively influences both histone H3S10 phosphorylation by ribosomal protein S6 kinase alpha-3 (RSK2) and histone H2BK5 acetylation by CREB-binding protein (CBP). HMGA1, RSK2, and CBP control the expression of a set of genes involved in tumor progression and epithelial to mesenchymal transition. These results suggest that HMGA1 has an effect on the epigenetic status of cancer cells and that it could be exploited as a responsiveness predictor for epigenetic therapies in triple-negative breast cancers

Transcriptional Regulation of Glucose Metabolism: The Emerging Role of the HMGA1 Chromatin Factor

HMGA1 (high mobility group A1) is a nonhistone architectural chromosomal protein that functions mainly as a dynamic regulator of chromatin structure and gene transcription. As such, HMGA1 is involved in a variety of fundamental cellular processes, including gene expression, epigenetic regulation, cell differentiation and proliferation, as well as DNA repair. In the last years, many reports have demonstrated a role of HMGA1 in the transcriptional regulation of several genes implicated in glucose homeostasis. Initially, it was proved that HMGA1 is essential for normal expression of the insulin receptor (INSR), a critical link in insulin action and glucose homeostasis. Later, it was demonstrated that HMGA1 is also a downstream nuclear target of the INSR signaling pathway, representing a novel mediator of insulin action and function at this level. Moreover, other observations have indicated the role of HMGA1 as a positive modulator of the Forkhead box protein O1 (FoxO1), a master regulatory factor for gluconeogenesis and glycogenolysis, as well as a positive regulator of the expression of insulin and of a series of circulating proteins that are involved in glucose counterregulation, such as the insulin growth factor binding protein 1 (IGFBP1), and the retinol binding protein 4 (RBP4). Thus, several lines of evidence underscore the importance of HMGA1 in the regulation of glucose production and disposal. Consistently, lack of HMGA1 causes insulin resistance and diabetes in humans and mice, while variations in the HMGA1 gene are associated with the risk of type 2 diabetes and metabolic syndrome, two highly prevalent diseases that share insulin resistance as a common pathogenetic mechanism. This review intends to give an overview about our current knowledge on the role of HMGA1 in glucose metabolism. Although research in this field is ongoing, many aspects still remain elusive. Future directions to improve our insights into the pathophysiology of glucose homeostasis may include epigenetic studies and the use of "omics" strategies. We believe that a more comprehensive understanding of HMGA1 and its networks may reveal interesting molecular links between glucose metabolism and other biological processes, such as cell proliferation and differentiation

HMGA1 promotes breast cancer angiogenesis supporting the stability, nuclear localization and transcriptional activity of FOXM1

Background Breast cancer is the most common malignancy in women worldwide. Among the breast cancer subtypes, triple-negative breast cancer (TNBC) is the most aggressive and the most difficult to treat. One of the master regulators in TNBC progression is the architectural transcription factor HMGA1. This study aimed to further explore the HMGA1 molecular network to identify molecular mechanisms involved in TNBC progression. Methods RNA from the MDA-MB-231 cell line, silenced for HMGA1 expression, was sequenced and, with a bioinformatic analysis, molecular partners HMGA1 could cooperate with in regulating common downstream gene networks were identified. Among the putative partners, the FOXM1 transcription factor was selected. The relationship occurring between HMGA1 and FOXM1 was explored by qRT-PCR, co-immunoprecipitation and protein stability assays. Subsequently, the transcriptional activity of HMGA1 and FOXM1 was analysed by luciferase assay on the VEGFA promoter. The impact on angiogenesis was assessed in vitro, evaluating the tube formation ability of endothelial cells exposed to the conditioned medium of MDA-MB-231 cells silenced for HMGA1 and FOXM1 and in vivo injecting MDA-MB-231 cells, silenced for the two factors, in zebrafish larvae. Results Here, we discover FOXM1 as a novel molecular partner of HMGA1 in regulating a gene network implicated in several breast cancer hallmarks. HMGA1 forms a complex with FOXM1 and stabilizes it in the nucleus, increasing its transcriptional activity on common target genes, among them, VEGFA, the main inducer of angiogenesis. Furthermore, we demonstrate that HMGA1 and FOXM1 synergistically drive breast cancer cells to promote tumor angiogenesis both in vitro in endothelial cells and in vivo in a zebrafish xenograft model. Moreover, using a dataset of breast cancer patients we show that the co-expression of HMGA1, FOXM1 and VEGFA is a negative prognostic factor of distant metastasis-free survival and relapse-free survival. Conclusions This study reveals FOXM1 as a crucial interactor of HMGA1 and proves that their cooperative action supports breast cancer aggressiveness, by promoting tumor angiogenesis. Therefore, the possibility to target HMGA1/FOXM1 in combination should represent an attractive therapeutic option to counteract breast cancer angiogenesis

The High Mobility Group A1 (HMGA1) Chromatin Architectural Factor Modulates Nuclear Stiffness in Breast Cancer Cells

13siPlasticity is an essential condition for cancer cells to invade surrounding tissues. The nucleus is the most rigid cellular organelle and it undergoes substantial deformations to get through environmental constrictions. Nuclear stiffness mostly depends on the nuclear lamina and chromatin, which in turn might be affected by nuclear architectural proteins. Among these is the HMGA1 (High Mobility Group A1) protein, a factor that plays a causal role in neoplastic transformation and that is able to disentangle heterochromatic domains by H1 displacement. Here we made use of atomic force microscopy to analyze the stiffness of breast cancer cellular models in which we modulated HMGA1 expression to investigate its role in regulating nuclear plasticity. Since histone H1 is the main modulator of chromatin structure and HMGA1 is a well-established histone H1 competitor, we correlated HMGA1 expression and cellular stiffness with histone H1 expression level, post-translational modifications, and nuclear distribution. Our results showed that HMGA1 expression level correlates with nuclear stiffness, is associated to histone H1 phosphorylation status, and alters both histone H1 chromatin distribution and expression. These data suggest that HMGA1 might promote chromatin relaxation through a histone H1-mediated mechanism strongly impacting on the invasiveness of cancer cells-openopenSenigagliesi B, Penzo C, Severino LU, Maraspini R, Petrosino S, Morales-Navarrete H, Pobega E, Ambrosetti E, Parisse P, Pegoraro S, Manfioletti G, Casalis L, Sgarra RSenigagliesi, Beatrice; Penzo, C; Severino, Lu; Maraspini, R; Petrosino, Sara; Morales-Navarrete, H; Pobega, E; Ambrosetti, E; Parisse, P; Pegoraro, S; Manfioletti, G; Casalis, L; Sgarra,

Chromatin Immunoprecipitation to Analyze DNA Binding Sites of HMGA2

BACKGROUND: HMGA2 is an architectonic transcription factor abundantly expressed during embryonic and fetal development and it is associated with the progression of malignant tumors. The protein harbours three basically charged DNA binding domains and an acidic protein binding C-terminal domain. DNA binding induces changes of DNA conformation and hence results in global overall change of gene expression patterns. Recently, using a PCR-based SELEX (Systematic Evolution of Ligands by Exponential Enrichment) procedure two consensus sequences for HMGA2 binding have been identified. METHODOLOGY/PRINCIPAL FINDINGS: In this investigation chromatin immunoprecipitation (ChIP) experiments and bioinformatic methods were used to analyze if these binding sequences can be verified on chromatin of living cells as well. CONCLUSION: After quantification of HMGA2 protein in different cell lines the colon cancer derived cell line HCT116 was chosen for further ChIP experiments because of its 3.4-fold higher HMGA2 protein level. 49 DNA fragments were obtained by ChIP. These fragments containing HMGA2 binding sites have been analyzed for their AT-content, location in the human genome and similarities to sequences generated by a SELEX study. The sequences show a significantly higher AT-content than the average of the human genome. The artificially generated SELEX sequences and short BLAST alignments (11 and 12 bp) of the ChIP fragments from living cells show similarities in their organization. The flanking regions are AT-rich, whereas a lower conservation is present in the center of the sequences

Frequent overexpression of HMGA1 and 2 in gastroenteropancreatic neuroendocrine tumours and its relationship to let-7 downregulation

The molecular pathogenesis of gastroenteropancreatic (GEP) neuroendocrine tumours (NETs) remains to be elucidated. High-mobility group A (HMGA) proteins play important roles in the regulation of transcription, differentiation, and neoplastic transformation. In this study, the expression of HMGA1 and HMGA2 was studied in 55 GEP NETs. Overexpression of HMGA1 and 2 was frequently detected in GEP NETs compared with normal tissues. Nuclear immunostaining of HMGA1 and 2 was observed in GEP NETs (38 of 55, 69%; 40 of 55, 73%, respectively). High-mobility group A2 expression increased from well-differentiated NET (WNET) to well-differentiated neuroendocrine carcinoma (WNEC) and poorly differentiated NEC (PNEC) (P<0.005) and showed the highest level in stage IV tumours (P<0.01). In WNECs, the expression of HMGA1 and 2 was significantly higher in metastatic tumours than those without metastasis (P<0.05). Gastroenteropancreatic NETs in foregut showed the highest level of HMGA1 and 2 expressions. MIB-1 labelling index (MIB-1 LI) correlated with HMGA1 and 2 overexpression (R=0.28, P<0.05; R=0.434, P<0.001; respectively) and progressively increased from WNETs to WNECs and PNECs (P<0.001). Let-7 expression was addressed in 6 normal organs, 30 tumour samples, and 24 tumour margin non-tumour tissues. Compared with normal tissues, let-7 downregulation was frequent in NETs (19 of 30, 63%). Higher expression of HMGA1 and 2 was frequently observed in tumours with let-7 significant reduction (53, 42%, respectively). The reverse correlation could be detected between HMGA1 and let-7 (P<0.05). Our findings suggested that HMGA1 and 2 overexpression and let-7 downregulation might relate to pathogenesis of GEP NETs

Identification of infrastructure related risk factors, Deliverable 5.1 of the H2020 project SafetyCube

The present Deliverable (D5.1) describes the identification and evaluation of infrastructure related risk factors. It outlines the results of Task 5.1 of WP5 of SafetyCube, which aimed to identify and evaluate infrastructure related risk factors and related road safety problems by (i) presenting a taxonomy of infrastructure related risks, (ii) identifying “hot topics” of concern for relevant stakeholders and (iii) evaluating the relative importance for road safety outcomes (crash risk, crash frequency and severity etc.) within the scientific literature for each identified risk factor. To help achieve this, Task 5.1 has initially exploited current knowledge (e.g. existing studies) and, where possible, existing accident data (macroscopic and in-depth) in order to identify and rank risk factors related to the road infrastructure. This information will help further on in WP5 to identify countermeasures for addressing these risk factors and finally to undertake an assessment of the effects of these countermeasures. In order to develop a comprehensive taxonomy of road infrastructure-related risks, an overview of infrastructure safety across Europe was undertaken to identify the main types of road infrastructure-related risks, using key resources and publications such as the European Road Safety Observatory (ERSO), The Handbook of Road Safety Measures (Elvik et al., 2009), the iRAP toolkit and the SWOV factsheets, to name a few. The taxonomy developed contained 59 specific risk factors within 16 general risk factors, all within 10 infrastructure elements. In addition to this, stakeholder consultations in the form of a series of workshops were undertaken to prioritise risk factors (‘hot topics’) based on the feedback from the stakeholders on which risk factors they considered to be the most important or most relevant in terms of road infrastructure safety. The stakeholders who attended the workshops had a wide range of backgrounds (e.g. government, industry, research, relevant consumer organisations etc.) and a wide range of interests and knowledge. The identified ‘hot topics’ were ranked in terms of importance (i.e. which would have the greatest effect on road safety). SafetyCube analysis will put the greatest emphasis on these topics (e.g. pedestrian/cyclist safety, crossings, visibility, removing obstacles). To evaluate the scientific literature, a methodology was developed in Work Package 3 of the SafetyCube project. WP5 has applied this methodology to road infrastructure risk factors. This uniformed approach facilitated systematic searching of the scientific literature and consistent evaluation of the evidence for each risk factor. The method included a literature search strategy, a ‘coding template’ to record key data and metadata from individual studies, and guidelines for summarising the findings (Martensen et al, 2016b). The main databases used in the WP5 literature search were Scopus and TRID, with some risk factors utilising additional database searches (e.g. Google Scholar, Science Direct). Studies using crash data were considered highest priority. Where a high number of studies were found, further selection criteria were applied to ensure the best quality studies were included in the analysis (e.g. key meta-analyses, recent studies, country origin, importance). Once the most relevant studies were identified for a risk factor, each study was coded within a template developed in WP3. Information coded for each study included road system element, basic study information, road user group information, study design, measures of exposure, measures of outcomes and types of effects. The information in the coded templates will be included in the relational database developed to serve as the main source (‘back end’) of the Decision Support System (DSS) being developed for SafetyCube. Each risk factor was assigned a secondary coding partner who would carry out the control procedure and would discuss with the primary coding partner any coding issues they had found. Once all studies were coded for a risk factor, a synopsis was created, synthesising the coded studies and outlining the main findings in the form of meta-analyses (where possible) or another type of comprehensive synthesis (e.g. vote-count analysis). Each synopsis consists of three sections: a 2 page summary (including abstract, overview of effects and analysis methods); a scientific overview (short literature synthesis, overview of studies, analysis methods and analysis of the effects) and finally supporting documents (e.g. details of literature search and comparison of available studies in detail, if relevant). To enrich the background information in the synopses, in-depth accident investigation data from a number of sources across Europe (i.e. GIDAS, CARE/CADaS) was sourced. Not all risk factors could be enhanced with this data, but where it was possible, the aim was to provide further information on the type of crash scenarios typically found in collisions where specific infrastructure-related risk factors are present. If present, this data was included in the synopsis for the specific risk factor. After undertaking the literature search and coding of the studies, it was found that for some risk factors, not enough detailed studies could be found to allow a synopsis to be written. Therefore, the revised number of specific risk factors that did have a synopsis written was 37, within 7 infrastructure elements. Nevertheless, the coded studies on the remaining risk factors will be included in the database to be accessible by the interested DSS users. At the start of each synopsis, the risk factor is assigned a colour code, which indicates how important this risk factor is in terms of the amount of evidence demonstrating its impact on road safety in terms of increasing crash risk or severity. The code can either be Red (very clear increased risk), Yellow (probably risky), Grey (unclear results) or Green (probably not risky). In total, eight risk factors were given a Red code (e.g. traffic volume, traffic composition, road surface deficiencies, shoulder deficiencies, workzone length, low curve radius), twenty were given a Yellow code (e.g. secondary crashes, risks associated with road type, narrow lane or median, roadside deficiencies, type of junction, design and visibility at junctions) seven were given a Grey code (e.g. congestion, frost and snow, densely spaced junctions etc.). The specific risk factors given the red code were found to be distributed across a range of infrastructure elements, demonstrating that the greatest risk is spread across several aspects of infrastructure design and traffic control. However, four ‘hot topics’ were rated as being risky, which were ‘small work-zone length’, ‘low curve radius’, ‘absence of shoulder’ and ‘narrow shoulder’. Some limitations were identified. Firstly, because of the method used to attribute colour code, it is in theory possible for a risk factor with a Yellow colour code to have a greater overall magnitude of impact on road safety than a risk factor coded Red. This would occur if studies reported a large impact of a risk factor but without sufficient consistency to allocate a red colour code. Road safety benefits should be expected from implementing measures to mitigate Yellow as well as Red coded infrastructure risks. Secondly, findings may have been limited by both the implemented literature search strategy and the quality of the studies identified, but this was to ensure the studies included were of sufficiently high quality to inform understanding of the risk factor. Finally, due to difficulties of finding relevant studies, it was not possible to evaluate the effects on road safety of all topics listed in the taxonomy. The next task of WP5 is to begin identifying measures that will counter the identified risk factors. Priority will be placed on investigating measures aimed to mitigate the risk factors identified as Red. The priority of risk factors in the Yellow category will depend on why they were assigned to this category and whether or not they are a hot topic

Inventory of assessed infrastructure risk factors and measures, Deliverable 5.4 of the H2020 project SafetyCube

Inventory of assessed infrastructure risk factors and measures, Deliverable 5.4 of the H2020 project SafetyCub