ZNF521 Enhances MLL-AF9-Dependent Hematopoietic Stem Cell Transformation in Acute Myeloid Leukemias by Altering the Gene Expression Landscape
06 Oct 2021-International Journal of Molecular Sciences (Multidisciplinary Digital Publishing Institute)-Vol. 22, Iss: 19, pp 10814
TL;DR: In this paper, the authors demonstrate that ZNF521 mRNA levels correlate with specific genetic aberrations: in particular, the highest expression is observed in AMLs bearing MLL rearrangements, while the lowest is detected in amLs with FLT3-ITD, NPM1, or CEBPα double mutations.
Abstract: Leukemias derived from the MLL-AF9 rearrangement rely on dysfunctional transcriptional networks. ZNF521, a transcription co-factor implicated in the control of hematopoiesis, has been proposed to sustain leukemic transformation in collaboration with other oncogenes. Here, we demonstrate that ZNF521 mRNA levels correlate with specific genetic aberrations: in particular, the highest expression is observed in AMLs bearing MLL rearrangements, while the lowest is detected in AMLs with FLT3-ITD, NPM1, or CEBPα double mutations. In cord blood-derived CD34+ cells, enforced expression of ZNF521 provides a significant proliferative advantage and enhances MLL-AF9 effects on the induction of proliferation and the expansion of leukemic progenitor cells. Transcriptome analysis of primary CD34+ cultures displayed subsets of genes up-regulated by MLL-AF9 or ZNF521 single transgene overexpression as well as in MLL-AF9/ZNF521 combinations, at either the early or late time points of an in vitro leukemogenesis model. The silencing of ZNF521 in the MLL-AF9 + THP-1 cell line coherently results in an impairment of growth and clonogenicity, recapitulating the effects observed in primary cells. Taken together, these results underscore a role for ZNF521 in sustaining the self-renewal of the immature AML compartment, most likely through the perturbation of the gene expression landscape, which ultimately favors the expansion of MLL-AF9-transformed leukemic clones.
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TL;DR: Recent advancements about bisphosphonates’ effects, especially zoledronate are discussed, analyzing the biochemical mechanisms and anti-tumor effects on AML model systems.
Abstract: Metabolic reprogramming represents a hallmark of tumorigenesis to sustain survival in harsh conditions, rapid growth and metastasis in order to resist to cancer therapies. These metabolic alterations involve glucose metabolism, known as the Warburg effect, increased glutaminolysis and enhanced amino acid and lipid metabolism, especially the cholesterol biosynthesis pathway known as the mevalonate pathway and these are upregulated in several cancer types, including acute myeloid leukemia (AML). In particular, it was demonstrated that the mevalonate pathway has a pivotal role in cellular transformation. Therefore, targeting this biochemical process with drugs such as statins represents a promising therapeutic strategy to be combined with other anticancer treatments. In the last decade, several studies have revealed that amino-bisphosphonates (BP), primarily used for bone fragility disorders, also exhibit potential anti-cancer activity in leukemic cells, as well as in patients with symptomatic multiple myeloma. Indeed, these compounds inhibit the farnesyl pyrophosphate synthase, a key enzyme in the mevalonate pathway, reducing isoprenoid formation of farnesyl pyrophosphate and geranylgeranyl pyrophosphate. This, in turn, inhibits the prenylation of small Guanosine Triphosphate-binding proteins, such as Ras, Rho, Rac, Rab, which are essential for regulating cell survival membrane ruffling and trafficking, interfering with cancer key signaling events involved in clonal expansion and maturation block of progenitor cells in myeloid hematological malignancies. Thus, in this review, we discuss the recent advancements about bisphosphonates’ effects, especially zoledronate, analyzing the biochemical mechanisms and anti-tumor effects on AML model systems. Future studies will be oriented to investigate the clinical relevance and significance of BP treatment in AML, representing an attractive therapeutic strategy that could be integrated into chemotherapy.
4 citations
TL;DR: In this paper , mesenchymal stem cells were derived from the Killian nasal polyp and were induced to osteoblastic or adipocyte differentiation for up to 20 days, showing a partial epithelial phenotype with low mRNA levels of I-CAM and significant increase of E-cad.
Abstract: Killian’s (antrochoanal) polyp is a unilateral nasal polypoid lesion of the maxillary sinus especially affecting children and young adults with unilateral nasal obstruction, pus discharge, and headache. Although its etiology is unclear, chronic inflammation, autoreactivity, allergies, and viral infections are implicated in its formation and development, causing nasal tissue remodeling. In this context, we isolated and cultured mesenchymal stem cells from surgical biopsies of three patients with Killian nasal polyp (KNP-MSCs) while healthy nasal tissue (HNT-MSCs) was used as control. Our results demonstrated that KNP-MSCs exhibited reduced cell proliferation compared to HNT-MSCs, and migrated less than the control, showing a partial epithelial phenotype with low mRNA levels of I-CAM and a significant increase of E-cad. Subsequently, both MSCs were induced to osteoblastic or adipocyte differentiation for up to 20 days. KNP-MSCs underwent to differentiate into osteoblasts but exhibited reduced ALP activity and calcium deposits and low mRNA levels of osteogenesis-associated genes compared to osteogenic induced-HNT-MSCs. Conversely, KNP-MSCs and HNT-MSCs have shown the same adipogenic differentiation potential, with a similar lipid droplet amount, adipocyte gene expression, and triacylglycerols content. Taken together, these results first demonstrated the cellular and molecular characterization of MSCs derived from the Killian nasal polyp.
2 citations
TL;DR: Analysis of RNA isolated from transduced cells by RNA-Seq and qRT-PCR revealed that several genes involved in growth, proliferation, migration and tumor invasiveness are differentially expressed following increased ZNF521 expression.
Abstract: Epithelial ovarian carcinoma (EOC) is the most lethal gynecological tumor, that almost inevitably relapses and develops chemo-resistance. A better understanding of molecular events underlying the biological behavior of this tumor, as well as identification of new biomarkers and therapeutic targets are the prerequisite to improve its clinical management. ZNF521 gene amplifications are present in >6% of OCs and its overexpression is associated with poor prognosis, suggesting that it may play an important role in OC. Increased ZNF521 expression resulted in an enhancement of OC HeyA8 and ES-2 cell growth and motility. Analysis of RNA isolated from transduced cells by RNA-Seq and qRT-PCR revealed that several genes involved in growth, proliferation, migration and tumor invasiveness are differentially expressed following increased ZNF521 expression. The data illustrate a novel biological role of ZNF521 in OC that, thanks to the early and easy detection by RNA-Seq, can be used as biomarker for identification and treatment of OC patients.
2 citations
TL;DR: Acute myeloid leukemia (AML) is a clonal malignant disorder of progenitor cells characterized by uncontrolled proliferation, dysregulation in the differentiation program, and inhibition of apoptosis mechanisms as discussed by the authors .
Abstract: Acute myeloid leukemia (AML) is a clonal malignant disorder of myeloid progenitor cells characterized by uncontrolled proliferation, dysregulation in the differentiation program, and inhibition of apoptosis mechanisms [...].
References
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TL;DR: The Gene Set Enrichment Analysis (GSEA) method as discussed by the authors focuses on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation.
Abstract: Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
34,830 citations
TL;DR: An analytical strategy is introduced, Gene Set Enrichment Analysis, designed to detect modest but coordinate changes in the expression of groups of functionally related genes, which identifies a set of genes involved in oxidative phosphorylation whose expression is coordinately decreased in human diabetic muscle.
Abstract: DNA microarrays can be used to identify gene expression changes characteristic of human disease. This is challenging, however, when relevant differences are subtle at the level of individual genes. We introduce an analytical strategy, Gene Set Enrichment Analysis, designed to detect modest but coordinate changes in the expression of groups of functionally related genes. Using this approach, we identify a set of genes involved in oxidative phosphorylation whose expression is coordinately decreased in human diabetic muscle. Expression of these genes is high at sites of insulin-mediated glucose disposal, activated by PGC-1α and correlated with total-body aerobic capacity. Our results associate this gene set with clinically important variation in human metabolism and illustrate the value of pathway relationships in the analysis of genomic profiling experiments.
7,997 citations
TL;DR: GEPIA (Gene Expression Profiling Interactive Analysis) fills in the gap between cancer genomics big data and the delivery of integrated information to end users, thus helping unleash the value of the current data resources.
Abstract: Tremendous amount of RNA sequencing data have been produced by large consortium projects such as TCGA and GTEx, creating new opportunities for data mining and deeper understanding of gene functions. While certain existing web servers are valuable and widely used, many expression analysis functions needed by experimental biologists are still not adequately addressed by these tools. We introduce GEPIA (Gene Expression Profiling Interactive Analysis), a web-based tool to deliver fast and customizable functionalities based on TCGA and GTEx data. GEPIA provides key interactive and customizable functions including differential expression analysis, profiling plotting, correlation analysis, patient survival analysis, similar gene detection and dimensionality reduction analysis. The comprehensive expression analyses with simple clicking through GEPIA greatly facilitate data mining in wide research areas, scientific discussion and the therapeutic discovery process. GEPIA fills in the gap between cancer genomics big data and the delivery of integrated information to end users, thus helping unleash the value of the current data resources. GEPIA is available at http://gepia.cancer-pku.cn/.
5,980 citations
TL;DR: An analysis of the physiological function of the identified genes indicates that the RP approach is powerful for identifying biologically relevant expression changes and can lead to a sharp reduction in the number of replicate experiments needed to obtain reproducible results.
1,557 citations
TL;DR: Gene-expression profiling allows a comprehensive classification of AML that includes previously identified genetically defined subgroups and a novel cluster with an adverse prognosis.
Abstract: background In patients with acute myeloid leukemia (AML) a combination of methods must be used to classify the disease, make therapeutic decisions, and determine the prognosis. However, this combined approach provides correct therapeutic and prognostic information in only 50 percent of cases. methods We determined the gene-expression profiles in samples of peripheral blood or bone marrow from 285 patients with AML using Affymetrix U133A GeneChips containing approximately 13,000 unique genes or expression-signature tags. Data analyses were carried out with Omniviz, significance analysis of microarrays, and prediction analysis of microarrays software. Statistical analyses were performed to determine the prognostic significance of cases of AML with specific molecular signatures. results Unsupervised cluster analyses identified 16 groups of patients with AML on the basis of molecular signatures. We identified the genes that defined these clusters and determined the minimal numbers of genes needed to identify prognostically important clusters with a high degree of accuracy. The clustering was driven by the presence of chromosomal lesions (e.g., t(8;21), t(15;17), and inv(16)), particular genetic mutations ( CEBPA ), and abnormal oncogene expression ( EVI1 ). We identified several novel clusters, some consisting of specimens with normal karyotypes. A unique cluster with a distinctive gene-expression signature included cases of AML with a poor treatment outcome. conclusions Gene-expression profiling allows a comprehensive classification of AML that includes previously identified genetically defined subgroups and a novel cluster with an adverse prognosis.
1,364 citations