scholarly journals Identification of a new gene regulatory circuit involving B cell receptor activated signaling using a combined analysis of experimental, clinical and global gene expression data

Oncotarget ◽  
2016 ◽  
Vol 7 (30) ◽  
pp. 47061-47081 ◽  
Author(s):  
Alexandra Schrader ◽  
Katharina Meyer ◽  
Neele Walther ◽  
Ailine Stolz ◽  
Maren Feist ◽  
...  
Keyword(s):  
Gene Expression ◽  
B Cell ◽  
Gene Expression Data ◽  
Global Gene Expression ◽  
Cell Receptor ◽  
B Cell Receptor ◽  
Expression Data ◽  
Regulatory Circuit ◽  
New Gene ◽  
Gene Regulatory

scholarly journals Tumor Vs Normal Gene Expression Profiles Identify Deregulated Pathways in ETV6/RUNX1 Positive Acute Lymphoblastic Leukemia: A Microarray-Based Meta-Analysis

Blood ◽  
2015 ◽  
Vol 126 (23) ◽  
pp. 4812-4812
Author(s):  
Thomas Heiden ◽  
Janette Nickel ◽  
Robert Preissner ◽  
Karl Seeger
Keyword(s):  
Gene Expression ◽  
Acute Lymphoblastic Leukemia ◽  
B Cell ◽  
Progenitor Cells ◽  
Gene Expression Data ◽  
Lymphoblastic Leukemia ◽  
Meta Analysis ◽  
Global Gene Expression ◽  
B Cell Development ◽  
Expression Data

Abstract Acute lymphoblastic leukemia (ALL), a clinically and biologically heterogeneous disease, is the most common type of childhood cancer. Approximately 25% of B-cell precursor ALL (BCP-ALL) carry the cryptic chromosomal translocation t(12;21)(p13;q22), the most frequent genetic aberration in pediatric ALL. This translocation combines two transcription factors and essential regulators of normal hematopoiesis, ETV6 and RUNX1, generating the fusion oncogene ETV6/RUNX1 (synonym TEL/AML1). Recent studies in various animal models have strengthened the view that ETV6/RUNX1 positive cells give rise to preleukemic clones with a differentiation block in the pro/pre-B stage of B cell development that, after acquisition of additional mutations, may transform into full malignancy. However, the effects triggered by the expression of ETV6/RUNX1 and the associated additional mutated genes are not well understood. We compared global gene expression data from ETV6/RUNX1 positive ALL and normal hematopoietic progenitor cells to identify expression signatures underlying the malignant phenotype. Our meta-analysis comprised Affymetrix HG-U133A und HG-U133 Plus 2.0 data from a total of 321 patients with ETV6/RUNX1 positive ALL, different ETV6/RUNX1 positive cell models, leukemias with 11q23 rearrangements and hyperdiploid ALL, as well as 251 samples from various normal controls including normal bone marrow, hematopoietic stem cells (HSCs) and normal B-(progenitor) cells (multipotent progenitor, pro-, pre-, immature-, naive-, centrocyte-, centroblast-, memory-, plasmablast-B cells). To eliminate potential lab batch effects, we performed RMA normalization of all microarray CEL files and next applied the batch effect removal algorithm ComBat. For determination of differentially expressed genes and pathway analyses, the R/Bioconductor packages limma and SPIA were used, respectively. We generated a gene expression landscape of the normal B-cell development and were able to show that pre-B cells are the closest normal counterpart of ETV6/RUNX1 positive ALL. Furthermore, in comparisons with normal HSCs the p53-, mTOR-, PI3K-AKT-, Apoptosis-, and JAK-STAT signaling pathways were found to be deregulated in those ALL. The combination of multiple global gene expression data sets from ETV6/RUNX1 positive ALL offers the possibility of an integrated large-scale meta-analysis and reveals novel insights into deregulated gene networks in this type of leukemia. Disclosures No relevant conflicts of interest to declare.

Inference of Gene Regulatory Networks by Topological Prior Information and Data Integration

Biotechnology ◽  
2019 ◽  
pp. 265-304
Author(s):  
David Correa Martins Jr. ◽  
Fabricio Martins Lopes ◽  
Shubhra Sankar Ray
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Prior Information ◽  
Heterogeneous Data ◽  
Data Sources ◽  
Expression Data ◽  
Heterogeneous Data Sources ◽  
Gene Regulatory

The inference of Gene Regulatory Networks (GRNs) is a very challenging problem which has attracted increasing attention since the development of high-throughput sequencing and gene expression measurement technologies. Many models and algorithms have been developed to identify GRNs using mainly gene expression profile as data source. As the gene expression data usually has limited number of samples and inherent noise, the integration of gene expression with several other sources of information can be vital for accurately inferring GRNs. For instance, some prior information about the overall topological structure of the GRN can guide inference techniques toward better results. In addition to gene expression data, recently biological information from heterogeneous data sources have been integrated by GRN inference methods as well. The objective of this chapter is to present an overview of GRN inference models and techniques with focus on incorporation of prior information such as, global and local topological features and integration of several heterogeneous data sources.

PAGeneRN

2020 ◽  
pp. 1052-1075 ◽  
Author(s):  
Dina Elsayad ◽  
A. Ali ◽  
Howida A. Shedeed ◽  
Mohamed F. Tolba
Keyword(s):  
Gene Expression ◽  
Data Analysis ◽  
Network Analysis ◽  
Gene Regulatory Network ◽  
Gene Expression Data ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Expression Data ◽  
Gene Expression Data Analysis ◽  
Gene Regulatory

The gene expression analysis is an important research area of Bioinformatics. The gene expression data analysis aims to understand the genes interacting phenomena, gene functionality and the genes mutations effect. The Gene regulatory network analysis is one of the gene expression data analysis tasks. Gene regulatory network aims to study the genes interactions topological organization. The regulatory network is critical for understanding the pathological phenotypes and the normal cell physiology. There are many researches that focus on gene regulatory network analysis but unfortunately some algorithms are affected by data size. Where, the algorithm runtime is proportional to the data size, therefore, some parallel algorithms are presented to enhance the algorithms runtime and efficiency. This work presents a background, mathematical models and comparisons about gene regulatory networks analysis different techniques. In addition, this work proposes Parallel Architecture for Gene Regulatory Network (PAGeneRN).

High ZAP-70 and Differential Expression of B-Cell Receptor Related Genes in Chronic Lymphocytic Leukemia with V3-21 Gene Usage.

Blood ◽  
2004 ◽  
Vol 104 (11) ◽  
pp. 773-773
Author(s):  
Dirk Kienle ◽  
Alexander Kröber ◽  
Dirk Winkler ◽  
Daniel Mertens ◽  
Annett Habermann ◽  
...  
Keyword(s):  
Gene Expression ◽  
B Cell ◽  
Cell Receptor ◽  
B Cell Receptor ◽  
Lymphocytic Leukemia ◽  
Mutation Status ◽  
Bcr Signaling ◽  
Gene Usage ◽  
Lower Expression

Abstract V3-21 gene usage defines a distinct genetic subgroup of chronic lymphocytic leukemia (CLL) characterized by a poor clinical outcome regardless of the VH mutation status. V3-21 cases exhibit a highly characteristic B-cell receptor (BCR) structure as demonstrated by homologous CDR3 sequences and a restricted use of VL genes implicating a common antigen involved in tumor pathogenesis of this specific CLL subgroup. To investigate the role of antigenic stimulation in the pathogenesis of V3-21 using CLL, we analyzed the quantitative expression of genes involved in BCR signaling (ZAP-70, SYK, BLNK, LYN, PI3K, PLCG2, FOS), B-cell activation (TRAF3, STAT6, NFKB), and cell cycle or apoptosis control (ATM, BCL-2, BAX, CDK4, CCND1, CCND2, CCND3, p27, E2F1, MYC) in V3-21 cases in comparison to VH mutated (VH MUT) and VH unmutated (VH UM) cases not using the V3-21 gene. To obtain native expression signatures we studied a non-CD19-purified (nPU) cohort (V3-21: 18 cases, equally divided into VH mutated and VH unmutated cases; VH MUT: 17; VH UM: 19) and, for verification, a CD19-purified (PU) cohort (V3-21: 10 cases, equally divided into VH mutated and unmutated; VH MUT: 12; VH UM: 16) to exclude a contamination of the results by non-tumor cells. All cases were analyzed by FISH for +3q, 6q-, +8q, 11q-, +12q, 13q-, 17p-, and t(11;14) to avoid major imbalances of genomic alterations between the subgroups under study. As expected, ZAP-70 expression was higher in VH UM as compared to VH MUT cases in the nPU (p=0.007) as well as the PU cohort (p=0.009). V3-21 cases showed a higher ZAP-70 expression as compared to VH MUT (nPU: p=0.033; PU: p=0.038). This applied also when restricting this comparison to V3-21 mutated cases (nPU: p=0.018). Median ZAP-70 expression in the PU cohort was 1.15 in VH MUT vs. 7.69 in VH UM cases, as compared to 7.05 in V3-21 cases (V3-21 mutated cases: 10.69; V3-21 unmutated: 6.7). Other genes differentially expressed between the V3-21 and VH MUT subgroups in nPU cases were PI3K (p=0.048), PLCG2 (p=0.007), CCND2 (p=0.003), p27 (p=0.003), BCL-2 (p=0.025), and ATM (p=0.006). In addition, a set of genes was detected with a differential expression between V3-21 and VH UM (nPU) including PLCG2 (p=0.014), NFKB (p=0.023), CCND2 (p=0.001), p27 (0.002), and BAX (p=0.028). Notably, except for ZAP-70, all of the differentially expressed genes showed a lower expression in V3-21 as compared to the other subgroups. When comparing the V3-21 mutated and V3-21 unmutated subgroups (nPU), there were no significant gene expression differences except for CDK4, which showed a lower expression in V3-21 unmutated cases. Therefore, cases with V3-21 usage appear to show a rather homogeneous gene expression pattern independently of the VH mutation status, which can be distinguished from VH MUT and VH UM cases not using V3-21. The expression differences observed suggest a role of differential BCR signaling in the pathogenesis of this distinct CLL subgroup. Deregulation of cell cycle, apoptosis, and candidate genes such as ATM indicate the involvement of additional pathways in the pathogenesis of CLL cases using V3-21.

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