scholarly journals Cellular Model of CEBPA N321D Captures Gene Expression Profile in the Transition to Pre-Leukaemic Status

Blood ◽  
2016 ◽  
Vol 128 (22) ◽  
pp. 3922-3922
Author(s):  
Moosa Qureshi ◽  
Wajid Jawaid ◽  
Fernando J Calero-Nieto ◽  
Rebecca Hannah ◽  
Sarah J Kinston ◽  
...  

Abstract Background C/EBPα plays a pivotal role in myeloid differentiation at the CMP to GMP transition point, where it interacts with other transcription factors (TFs) implicated in haematopoiesis. CEBPA mutations are common in acute myeloid leukaemia (AML), predominantly in patients with M1 and M2 French-American-British (FAB) morphological classifications, but relatively little is understood about the pre-leukaemic alterations caused by mutated CEBPA. Murine models have established N321D as a particularly potent CEBPA mutation which causes AML with high mortality (Togami et al, Experimental Hematology, 2015). We aimed to develop an inducible expression system for CEBPA N321D in a cellular model which replicates early haematopoietic progenitors, to study the effects of this mutation on gene expression profiles relevant for malignant haematopoiesis. Methods We constructed a Piggy-bac Tet-on inducible expression system which has a 2A peptide mechanism enabling simultaneous expression of both N321D and mCherry fluorescent protein from the same transcript (Fig. 1). We also constructed a control with inducible expression of mCherry. These two plasmids were then transfected into the mouse progenitor cell line Hoxb8-FL (Redeckeet al, Nature Methods, 2013), which is conditionally immortalized and models multipotent myelo-lymphoid progenitors. Single cell clones were established and selected for analysis on the basis of cell growth and mCherry fluorescence on induction. RNA was collected post-induction and without induction at 24, 48 and 72 hours in two replicates each from the N321D clone and from the empty control vector. RNA-seq data was aligned to the mouse genome using STAR aligner, processed to generate high throughput sequencing counts, and finally differential expression analysis was performed between N321D and the control. Results Differential expression analysis identified 172 downregulated and 60 upregulated genes after N321D induction. Further analysis of the 172 downregulated genes against online published datasets of gene expression (Gene Expression Commons, https://gexc.stanford.edu), revealed that 19 of these genes are normally upregulated at the CMP to GMP transition. These include genes such as Hck, Met, Hdac8 and Kdm7a which have been previously implicated in haematological malignancy and which may provide novel insights into the leukaemic process fostered by the CEBPA N321D mutation. To further validate our data, we performed unsupervised hierarchical clustering of previously published microarray data from a large collection of over 400 AML expression profiles (Verhaaket al, Haematologica, 2009) using the genes identified in our study, and found that patient samples who had predominantly FAB classifications M1 and M2 clustered together (Fig. 2A,B), as would be expected in CEBPA-mutated AML. Conclusions Our inducible expression system has the potential to provide novel insights into altered gene expression caused by induction of mutated CEBPA. In particular, our cellular model replicates an early stage of haematopoiesis, and implicates genes which were not previously known to interact with CEBPA. The importance of these genes in CEBPA N321D-mediated re-configuration of the myeloid transcriptional regulatory network requires further analysis. Disclosures No relevant conflicts of interest to declare.

2016 ◽  
Vol 36 (suppl_1) ◽  
Author(s):  
Elisa C Maruko ◽  
Hao Xu ◽  
Sushma Kaul ◽  
Brian J Capaldo ◽  
Nathalie Pamir ◽  
...  

Atherosclerosis is a disease of both lipids and inflammatory immune cells. More specifically, elevated plasma levels of low-density lipoproteins (LDL) leads to migration of circulating monocytes into the artery wall. Lipid loaded monocyte cells subsequently proliferate in the arterial walls becoming macrophage foam cells; a hallmark of atherosclerotic lesions. A proposed mechanism of the protective effects of high-density lipoprotein (HDL) is apolipoprotein A-I (apo A-I) acting as a mediator of cholesterol efflux and subsequent foam cell regression. To better understand the biological changes stimulated by apo A-I treatment, differential expression analysis of microarray data was performed on spleen cells from apo A-I treated mice. LDL receptor null (LDLr -/- ) and LDL receptor and apo A-I null (LDLr -/- , apoA-I -/- ) mice were fed a western diet consisting of 0.2% cholesterol and 42% of calories as fat for 12 weeks. After 6 weeks of diet, a subset of mice for each genotype was subcutaneously injected with 200 micrograms of apo A-I 3 times a week for the remaining 6 weeks. The control group mice were subcutaneously injected with 200 micrograms of saline or BSA. Spleen cell RNA was isolated, purified, and analyzed for differential expression analysis using Illumina BeadArray Microarray Technology Analysis. Individual gene expression analysis for LDLr -/- , apoA-I -/- apo A-I treated mice showed 281 significantly differentially expressed genes compared to BSA treated mice. LDLr -/- A-I treated mice had 1502. Of the significant genes, 189 intersected across both genotypes. LDLr -/- , apoA-I -/- A-I mice showed 73 up-regulated and 116 down-regulated genes. Similarly, LDLr -/- A-I mice had 71 up-regulated and 118 down-regulated. One-directional Gene Set Enrichment Analysis (GSEA) of LDLr -/- , apoA-I -/- A-I mice revealed 49 significant pathways while a total of 63 were found for LDLr -/- . Of these pathways, 21 were up-regulated and 13 were down-regulated in both genotypes. Eight of the top 10 most significant up-regulated pathways in both genotypes were immune cell related. Their functions involve receptor, adhesion, and chemokine signaling. Overall, preliminary analysis suggests A-I treatment induces similar gene expression changes across different genotypes.


2012 ◽  
Vol 78 (7) ◽  
pp. 2100-2105 ◽  
Author(s):  
Dorthe Kixmüller ◽  
Jörg-Christian Greie

ABSTRACTGradually inducible expression vectors which are governed by variations of growth conditions are powerful tools for gene expression of conditionally lethal mutants. Furthermore, controlled expression allows monitoring of overproduction of proteins at various stages in their expressing hosts. ForHalobacterium salinarum, which is often used as a paradigm for halophilic archaea, such an inducible expression system is not available to date. Here we show that thekdppromoter (Pkdp), which facilitates gene expression upon K+limitation, can be used to establish such a system for molecular applications. Pkdpfeatures a rather high expression rate, with an approximately 50-fold increase that can be easily varied by K+concentrations in the growth medium. Besides the construction of an expression vector, our work describes the characterization of expression patterns and, thus, offers a gradually inducible expression system to the scientific community.


2015 ◽  
Vol 9s3 ◽  
pp. BBI.S29470 ◽  
Author(s):  
Mikhail G. Dozmorov ◽  
Nicolas Dominguez ◽  
Krista Bean ◽  
Susan R. Macwana ◽  
Virginia Roberts ◽  
...  

Systemic lupus erythematosus (SLE) is an autoimmune disease characterized by complex interplay among immune cell types. SLE activity is experimentally assessed by several blood tests, including gene expression profiling of heterogeneous populations of cells in peripheral blood. To better understand the contribution of different cell types in SLE pathogenesis, we applied the two methods in cell-type-specific differential expression analysis, csSAM and DSection, to identify cell-type-specific gene expression differences in heterogeneous gene expression measures obtained using RNA-seq technology. We identified B-cell-, monocyte-, and neutrophil-specific gene expression differences. Immunoglobulin-coding gene expression was altered in B-cells, while a ribosomal signature was prominent in monocytes. On the contrary, genes differentially expressed in the heterogeneous mixture of cells did not show any functional enrichment. Our results identify antigen binding and structural constituents of ribosomes as functions altered by B-cell- and monocyte-specific gene expression differences, respectively. Finally, these results position both csSAM and DSection methods as viable techniques for cell-type-specific differential expression analysis, which may help uncover pathogenic, cell-type-specific processes in SLE.


F1000Research ◽  
2017 ◽  
Vol 6 ◽  
pp. 2010 ◽  
Author(s):  
Monther Alhamdoosh ◽  
Charity W. Law ◽  
Luyi Tian ◽  
Julie M. Sheridan ◽  
Milica Ng ◽  
...  

Gene set enrichment analysis is a popular approach for prioritising the biological processes perturbed in genomic datasets. The Bioconductor project hosts over 80 software packages capable of gene set analysis. Most of these packages search for enriched signatures amongst differentially regulated genes to reveal higher level biological themes that may be missed when focusing only on evidence from individual genes. With so many different methods on offer, choosing the best algorithm and visualization approach can be challenging. The EGSEA package solves this problem by combining results from up to 12 prominent gene set testing algorithms to obtain a consensus ranking of biologically relevant results.This workflow demonstrates how EGSEA can extend limma-based differential expression analyses for RNA-seq and microarray data using experiments that profile 3 distinct cell populations important for studying the origins of breast cancer. Following data normalization and set-up of an appropriate linear model for differential expression analysis, EGSEA builds gene signature specific indexes that link a wide range of mouse or human gene set collections obtained from MSigDB, GeneSetDB and KEGG to the gene expression data being investigated. EGSEA is then configured and the ensemble enrichment analysis run, returning an object that can be queried using several S4 methods for ranking gene sets and visualizing results via heatmaps, KEGG pathway views, GO graphs, scatter plots and bar plots. Finally, an HTML report that combines these displays can fast-track the sharing of results with collaborators, and thus expedite downstream biological validation. EGSEA is simple to use and can be easily integrated with existing gene expression analysis pipelines for both human and mouse data.


F1000Research ◽  
2020 ◽  
Vol 9 ◽  
pp. 1444
Author(s):  
Charity W. Law ◽  
Kathleen Zeglinski ◽  
Xueyi Dong ◽  
Monther Alhamdoosh ◽  
Gordon K. Smyth ◽  
...  

Differential expression analysis of genomic data types, such as RNA-sequencing experiments, use linear models to determine the size and direction of the changes in gene expression. For RNA-sequencing, there are several established software packages for this purpose accompanied with analysis pipelines that are well described. However, there are two crucial steps in the analysis process that can be a stumbling block for many -- the set up an appropriate model via design matrices and the set up of comparisons of interest via contrast matrices. These steps are particularly troublesome because an extensive catalogue for design and contrast matrices does not currently exist. One would usually search for example case studies across different platforms and mix and match the advice from those sources to suit the dataset they have at hand. This article guides the reader through the basics of how to set up design and contrast matrices. We take a practical approach by providing code and graphical representation of each case study, starting with simpler examples (e.g. models with a single explanatory variable) and move onto more complex ones (e.g. interaction models, mixed effects models, higher order time series and cyclical models). Although our work has been written specifically with a limma-style pipeline in mind, most of it is also applicable to other software packages for differential expression analysis, and the ideas covered can be adapted to data analysis of other high-throughput technologies. Where appropriate, we explain the interpretation and differences between models to aid readers in their own model choices. Unnecessary jargon and theory is omitted where possible so that our work is accessible to a wide audience of readers, from beginners to those with experience in genomics data analysis.


2015 ◽  
Vol 11 (5) ◽  
pp. 1235-1240 ◽  
Author(s):  
Xi Wang ◽  
Erin J. Gardiner ◽  
Murray J. Cairns

Reference gene-based normalization of expression profiles secures consistent differential expression analysis between samples of different phenotypes or biological conditions, and facilitates comparison between experimental batches.


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