research paper

Differential gene expression analysis in early and late erythroid progenitor cells in b-thalassaemia

Luke Forster,1 John McCooke,2 Matthew Bellgard,2 David Joske,3 Jill Finlayson1,3 and Reza Ghassemifar1,3 1

School of Pathology and Laboratory Medicine,

University of Western Australia, Nedlands, 2

Centre for Comparative Genomics, Murdoch

University, Murdoch, and 3Department of Haematology, PathWest Laboratory Medicine, Queen Elizabeth II Medical Centre, Nedlands, WA, Australia Received 8 December 2014; accepted for publication 19 February 2015 Correspondence: Associate Professor Dr. Reza Ghassemifar, Head of Experimental Haematology Research Unit, Department of Haematology, PathWest Laboratory Medicine WA, Nedlands, WA 6009, Australia. E-mail: [email protected]

Summary b- thalassaemia is a disorder of globin gene synthesis resulting in reduced or absent production of the b-globin chain in red blood cells. In this study, haematopoietic stem cells were isolated from the peripheral blood of six transfusion dependent b-thalassaemia patients and six healthy controls. Following 7 and 14 d in culture, early- and late- erythroblasts were isolated and purified. No morphological difference in maturation was observed following 7 d in culture, while a delayed maturation was observed in the patient group after 14 d. Following RNA isolation and linear amplification, gene expression analyses were performed using microarray technology. The generated data were analysed by two methods: the BRB-ArrayTools platform and the Bioconductor platform using bead level data. Following 7 d culture, there was no difference in gene expression between the control and patient groups. Following 14 d culture, 384 differentially expressed genes were identified by either analysis. A subset of 90 genes was selected and the results were confirmed by Quantitative-Real-Time-polymerase chain reaction. Pathways shown to be significantly altered in the patient group include apoptosis, MAPKinase and the nuclear factor-jB pathway. Keywords: b-thalassaemia, microarray, quantitative real time PCR, gene expression, erythropoiesis.

Erythropoiesis describes the processes involved in the lineage commitment, maturation and terminal differentiation of a haematopoietic stem cell (HSC) into a mature red blood cell (RBC).( Koury et al, 2002; Ney, 2006; Goh et al, 2007) This maturation process involves the progression through distinct and definable transitional phases. Initially, a common myeloid progenitor (CMP) is generated from a HSC. This further differentiates into a megakaryocytic/erythroid progenitor (MEP) before becoming the erythroid lineage-specific burst-forming unit-erythroid (BFUE), which differentiates further to become a colony-forming unit-erythroid (CFU-E). These then differentiate through erythroblast phases of increasing maturity, namely pro- (ProE), basophilic- (Baso-E), polychromatic- (Poly-E) and orthochromatic- (Ortho-E) erythroblasts. During this maturation phase, the cells decrease in size, undergo membrane reorganization, chromatin condensation and nucleus reduction, while haemoglobinization increases. The cell then expels its nucleus to become a reticulocyte, the last step before becoming a biconcave red blood cell. ª 2015 John Wiley & Sons Ltd British Journal of Haematology, 2015, 170, 257–267

The b-thalassaemias are a heterogeneous group of hereditary blood disorders characterized by an imbalance in globin synthesis caused by reduced (b+) or absent (b0) production of the b-globin chain of the adult haemoglobin tetramer. (Thein et al, 1990; Weatherall & Clegg, 2001; Galanello & Origa, 2010) There are 837 reported HBB (b-globin gene) mutations, of which 247 have been reported to cause the b-thalassaemia phenotype. (Patrinos et al, 2004) These can affect any point from expression of the HBB through to protein synthesis. Three haematological conditions are recognized based on increasing clinical severity. These are known as the carrier state, thalassaemia intermedia and thalassaemia major. (Rund & Rachmilewitz, 2005) The patients selected for our study have transfusion-dependent b-thalassaemia major. The severity of the disease is directly related to the imbalance between the globin chains, and a major factor in the pathogenesis of the disorder is the precipitation of the excess a-globin chains, leading to oxidative damage of the cell membrane of the RBC precursor with resultant ineffective erythropoiesis. (Rund & Rachmilewitz,

First published online 19 April 2015 doi: 10.1111/bjh.13432

L. Forster et al 2005; Thein, 2008; Galanello & Origa, 2010; Higgs et al, 2012) Traditionally, the pathogenesis of b-thalassaemia has been attributed to ineffective erythropoiesis due to intramedullary apoptosis of the erythroid progenitors. Recently, studies in mouse models have challenged this hypothesis and the concept of delayed maturation of the erythroid progenitors has been put forward as a contributing factor to the ineffective erythropoiesis.(Libani et al, 2008). Whole-genome microarray analysis is an area of increasing importance for understanding how the altered expression of genetic variants contributes to the formation of complex diseases, such as cancer, diabetes and heart disease.(Nygaard & Hovig, 2006; Trachtenberg et al, 2012) Detection of differences and alterations in expression profiles through the analysis of genome-wide RNA expression provides greater insights into biological pathways and molecular mechanisms that regulate cell fate, development and disease progression. This technology is capable of simultaneously determining the expression levels of tens of thousands of well-characterized genes, gene candidates and splice variants. While previous studies have focused on gene expression differences in normal human haematopoiesis,(Komor et al, 2005; Merryweather-Clarke et al, 2011) we have utilized this technology to assess the differences in gene expression between b-thalassaemic and normal erythroid progenitor cells, providing important information on the biological processes which play a role in the pathogenesis of b-thalassaemia as well as identifying a list of differentially expressed genes for further study.

Material and methods Patient inclusion All patients included in this study had transfusion-dependent b-thalassaemia major. One patient (T2) is homozygous for HBB:c.92 + 5G>C (Kazazian et al, 1984; Divoky et al, 1992), three patients (T3-5) were homozygous for HBB:c.93-21G>A (Spritz et al, 1981; Baysal et al, 1992) and two patients

(T1 + T6) were heterozygous for HBB:c.93-21G>A and HBB: c.118C>T, p. Gln40STOP [(Orkin & Goff, 1981; Trecartin et al, 1981). The patients were tested for the common alpha thalassaemia deletions as well as the triplicated alpha globin genes and all were normal. HBVar (Database of Human Hemoglobin Variants and Thalassemia Mutation; http://globin.bx.psu.edu/hbvar/] identifiers (HBVar IDs) and b0/B+ statuses are listed in Table I. Ethics approval was obtained from the Sir Charles Gairdner Hospital Human Research Ethics Committee (2008–126) and the Human Research Ethics Office of The University of Western Australia (RA/4/1/ 5423). All participants gave written informed consent.

Selection of CD34+ haematopoietic stem cells Heparinized peripheral blood samples were collected from healthy controls (20 ml) and patients (10 ml) prior to transfusion. Red blood cells were removed by lysing with Pharmlyse (BD Biosciences, San Jose, CA, USA) and washing twice with HBSS (Gibco, Grand Island, NY, USA). CD34+ cells were enriched by high-gradient magnetic cell column separation using CD34+ MicroBeads (Miltenyi Biotec, BergischGladbach, Germany) as per the manufacturer’s instructions.

Cell culture and isolation Cells were cultured in Iscove’s modified Dulbecco’s culture medium (IMDM) supplemented with 2 mM l-glutamine, 30% fetal bovine serum and 1% bovine serum albumin. 1% methylcellulose was included to create a semi-solid medium (Miltenyi Biotec). Important growth factors included were stem cell factor (SCF, also termed KITLG; 50 ng/ml), granulocyte colony-stimulating factor (GM-CSF; 20 ng/ml), granulocyte colony-stimulating factor (G-CSF; 20 ng/ml), interleukin (IL) 3 (20 ng/ml), IL6 (20 ng/ml) and erythropoietin (EPO; 3 u/ml) (Miltenyi Biotec). The cells were cultured in 6-well plates containing 11 ml of media and 500 CD34+ cells, incubated at 37°C in air with 5% CO2. Two wells contained water to provide a humidified environment. The cells were inspected daily and harvested at predefined

Table I. Patient information. Patient

HGVS name

HBVar description

HbVar ID

b0/b+

a-globin details

T1

HBB:c.93-21G>A (heterozygous) HBB:c.118C>T, p. Gln40STOP (heterozygous) HBB:c.92 + 5G>C (homozygous) HBB:c.93-21G>A (homozygous) HBB:c.93-21G>A (homozygous) HBB:c.93-21G>A (heterozygous) HBB:c.118C>T, p. Gln40STOP (heterozygous) HBB:c.93-21G>A (homozygous)

IVS-I-110 (G->A) Codon 39 (C->T); CAG(Gln)->TAG(stop codon) IVS-I-5 (G->C) IVS-I-110 (G->A) IVS-I-110 (G->A) IVS-I-110 (G->A) Codon 39 (C->T); CAG(Gln)->TAG(stop codon) IVS-I-110 (G->A)

827 845

b+ b0

Normal: aa/aa

824 827 827 827 845

b+ b+ b+ b+ b0

Normal: Normal: Normal: Normal:

827

b+

Normal: aa/aa

T2 T3 T4 T5

T6

aa/aa aa/aa aa/aa aa/aa

HGVS, Human Genome Variation Society; HBVar, Database of Human Hemoglobin Variants and Thalassemia Mutations.

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Gene Expression in Thalassaemic Progenitor Cells time points (7 and 14 d). Cells were enriched for erythroid origin by high-gradient magnetic cell column separation using CD71+ microbeads (Miltenyi Biotec) on day 7 and CD235a+ microbeads (Miltenyi Biotec) on day 14. Cells were immediately processed for RNA isolation.

Flow cytometry and morphological analysis CD34+ cells were counted using Trucount tubes containing anti-CD34 conjugated to phycoerythrin (PE) and anti-CD45conjugated to fluorescein isothiocynate (FITC) antibodies and the exclusion dye 7-Amino-Actinomycin (7AAD) (BD Biosciences). Erythroid lineage cells were classified by staining with CD71-FITC, CD64-PE, CD34-peridinin chlorophyll-cyanin 55 (PerCP-Cy55), CD33-PE-Cy7, CD235a-allophycocyanin (APC) and CD45-V500 (BD Biosciences). Isotype controls were also prepared. Analysis gates were set to exclude dead cells and debris. All assays were run on a FACSCanto (BD Biosciences). Twenty thousand cells were used for cytospin analysis. Cytospins were stained with May-Grunwald–Giemsa and analysed by light microscopy.

RNA isolation, amplification and microarray hybridization Total RNA was isolated using the mirVana miRNA Isolation Kit (Ambion, Austin, TX, USA) as per the manufacturer’s instructions. The samples were DNase treated to remove trace genomic DNA contamination using the DNA-free DNase treatment kit (Ambion). The integrity of the RNA was confirmed using the Bioanalyzer 2100 (Agilent, Palo Alto, CA, USA). The RNA was linearly amplified and biotinylated using the Illumina TotalPrep RNA amplification kit (Ambion). Amplified RNA was hybridized to the Human HT-12 Expression BeadChip (Illumina, San Diego, CA, USA) as per the manufacturer’s instruction. The arrays were run on an iScan (Illumina).

Data analysis Data analysis was performed by two complementary methods. Initially, non-normalized raw data with no background subtraction was imported from the GenomeStudio software (Illumina) into the National Cancer Institute Biometrics Research Branch (BRB)-ArrayTools analysis software.(Simon et al, 2007; Xu et al, 2008) The software integrates Excel add-ins with the statistical processing language R. Loess normalization was then conducted using the Bioconductor Lumi software package (http://www.bioconductor.org) (Gentleman et al, 2004; Ritchie et al, 2011). The data was filtered so that genes were excluded if less than 20% of the expression data had less than a 15-fold change in gene expression in either direction from the gene’s median value. Differentially ª 2015 John Wiley & Sons Ltd British Journal of Haematology, 2015, 170, 257–267

expressed genes were required to have a P-value of at least 0001 following univariate analyses. Concurrently, raw bead level data was processed using Bioconductor packages (Gentleman et al, 2004; Ritchie et al, 2011) in R with spatial artefacts identified and removed using BASH (BeadArray Subversion of Harshlight; Cairns et al, 2008) and HULK. (Lynch et al, 2009) Quantile normalization and statistical analysis of differential expression was calculated using the Linear Models for Microarray Data (LIMMA) package (Smyth, 2004, 2005) from Bioconductor. Differentially expressed genes were filtered by log-odds of over 2. Gene ontology interrogation was performed using gene data within DAVID (Database for Annotation, Visualization and Integrated Discovery; da Huang et al, 2009a), KEGG (Kyoto Encyclopedia of Genes and Genomes; Kanehisa & Goto, 2000), Gene Ontology (GO) database (Harris et al, 2004) and BioCarta.(Nishimura, 2001) The data for this study is available through the National Center for Biotechnology Information Gene Expression Omnibus.(Edgar et al, 2002) The SuperSeries accession number which links both analyses methods is GSE62431 http://www.ncbi.nlm.nih.gov/ geo/query/acc.cgi?acc=GSE62431. Each analysis method can also be accessed individually at http://www.ncbi.nlm.nih.gov/ geo/query/acc.cgi?acc=GSE56088 and http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE62430.

Quantitative real time polymerase chain reaction (QReTi-PCR) Complementary DNA was synthesized from 250 ng of RNA using the RT2 First Strand Kit (SABiosciences, Valencia, CA, USA) according to the manufacturer’s instruction, but excluding the DNase step and increasing the reaction volume to 26 ll. PCR arrays (SABiosciences) containing gene-specific primers for 90 genes identified as being differentially expressed by microarray analysis were included alongside five housekeeping genes, reverse transcription and genomic DNA controls (Table SI). Amplification was performed using RT² SYBR Green qPCR Mastermix (SABiosciences). Normalization was performed using NONO, PPIH and HPRT1 as these were shown to be suitable by internal validation (data not shown). Analysis was performed by the DDCT method (Livak & Schmittgen, 2001).

Results Cytospin morphology Morphology was determined by light microscopy following CD71 (also termed TFRC) (day 7) or CD235 (day 14) isolation (Fig 1). At day 7 there was no difference in morphology between the patient and control samples, with the majority of cells (>90%) being Pro-E. It should be noted that there was some low-level myeloid lineage contamination, as 259

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Flow cytometry Post-selection flow cytometry results were similar for both patients and controls at day 7 and day 14. Mean purity, as determined by percentage of CD235+ cells, was 91% and 88% for control and patients at day 7 increasing to 92% and 94% by day 14. Example plots are detailed in Fig S1.

Clustering of samples Using hierarchal clustering to organize the samples based on their similarities, an analogous pattern to that of the cytospin results was observed. Firstly, day 7 and 14 samples separated into two distinct groups. Within the day 7 group, control and patient samples integrated together. By day 14 the control and patient samples formed distinct groups (Fig 2). Figure S2 is a Multi-Dimensional Scaling (MDS) plot detailing these associations.

Differentially expressed genes Under these conditions, at day 7, only one gene was differentially expressed (DEPDC1) between the patient and control 260

C1_D7 C2_D7 C3_D7 T6_D7 T4_D7 T5_D7 T2_D7 C4_D7 T3_D7 C6_D7 C5_D7 T1_D7 T2_D14 T3_D14 T5_D14 T4_D14 T6_D14 T1_D14 C2_D14 C6_D14 C4_D14 C3_D14 C1_D14 C5_D14

although CD71 is highly expressed on early erythroblasts, it is not specific. After 14 d in culture there was a notable difference between the groups, with the control samples showing advanced maturation compared to the patients. There were 7% Poly-E and 91% Ortho-E in the control group, compared with 27% Poly-E and 72% Ortho-E in the patient group, these differences were statistically significant as determined by the Student’s t-test (P = 0013 for Poly-E and P = 0015 Ortho-E).

Euclidean distance 0 14 30 46 62 78 94 112 132

Fig 1. Typical cytospin images from each culture period. Day 7 cytospins show no variation between control and patient samples. By day 14, the control samples were almost exclusively orthochromatic erythroblasts (Ortho-E) while the patient samples were a mixed population of polychromatic erythroblasts (Poly-E) and Ortho-E. Images are magnified 100x.

Day 7

Day 14 Control

Fig 2. Clustergram generated by BRB-ArrayTools. Initially, the samples separate out into separate groups at day 7 and day 14 of culture. Within these groups, at day 7 there is no separation between the control and patient groups. By day 14, however, distinct patient and controls groups are visible. Similar results were obtained from bead level data analysis (data not shown). C indicates control, T indicates thalassaemic patient.

groups. However, by day 14 there were, in total, 384 differentially expressed genes. 290 of these were down-regulated in patients, while 94 were up-regulated. Of these differentially expressed genes, a core group of 47 genes was detected by both analysis methods (Table II). The full gene lists are detailed in Tables SII and SIII. A heat map of these differentially expressed genes generated with BRB-ArrayTools is shown in Fig 3, these have been clustered based on their expression patterns. ª 2015 John Wiley & Sons Ltd British Journal of Haematology, 2015, 170, 257–267

Gene Expression in Thalassaemic Progenitor Cells Table II. List of 47 genes that showed concordant differential expression in both analysis methods.

Localisation

NCBI gene ID

Description

Upregulated in patient ILMN_1804445 ATF7IP2 ILMN_1746819 C5 ILMN_2409395 CCNC ILMN_1776337 CHORDC1 ILMN_1740171 DUSP11 ILMN_2150284 RNPC3 ILMN_1765578 TIPARP

16p13.13 9q33-q34 6q21 11q14.3 2p13.1 1p21 3q25.31

80063 727 892 26973 8446 55599 25976

Activating transcription factor 7 interacting protein 2 Complement component 5 Cyclin C Cysteine and histidine-rich domain (CHORD) containing 1 Dual specificity phosphatase 11 (RNA/RNP complex 1-interacting) RNA-binding region (RNP1, RRM) containing 3 TCDD-inducible poly(ADP-ribose) polymerase

34 40 43 32 27 33 36

Downregulated in patient ILMN_1712944 AES ILMN_1690884 APOA1 ILMN_1669113 ATF5 ILMN_1812073 ATP6V1B1 ILMN_1794072 B3GAT1 ILMN_1710514 BCL3 ILMN_2331062 CBFA2T2 ILMN_1732831 CHST7 ILMN_1656501 DUSP5 ILMN_1747499 EMID1 ILMN_1717063 FBXO9 ILMN_1747589 HIST2H2AB ILMN_1802434 JPH2 ILMN_2086077 JUNB ILMN_1715814 LMAN1 ILMN_3289346 LOC442075 ILMN_1805395 LTBP3 ILMN_2316278 MAGED4B ILMN_3309473 MIR101-1 ILMN_1804448 MSI2 ILMN_1691156 MT1A ILMN_1799062 NFKB2

19p13.3 11q23-q24 19q13.3 2p13.1 11q25 19q13.1-q13.2 20q11 Xp11.23 10q25 22q12.2 6p12.3-p11.2 1q21 20q13.12 19p13.2 18q21.3-q22 3p25.3 11q13.1 Xp11 1p31.3 17q22 16q13 10q24

166 335 22809 525 27087 602 9139 56548 1847 129080 26268 317772 57158 3726 3998 442075 4054 81557 406893 124540 4489 4791

28 45 68 30 31 49 49 47 61 28 24 30 27 59 31 48 51 29 49 29 32 31

9p13.3 1q21.1 1q23.2 10p12.2 8p23.1 18p11.22 6p23 1q42.13 11q13 1p35.3 1p34.3 6q24.3 Xp22.3 8q11.2 17q11.2 2p24.3 14q22-q24 7q36.1

65083 440672 93183 5305 79660 9989 221687 149603 10313 83667 83931 134957 6907 6917 7067 28951 677 27153

Amino-terminal enhancer of split Apolipoprotein A-I Activating transcription factor 5 ATPase, H+ transporting, lysosomal 56/58 kDa, V1 subunit B1 beta-1,3-glucuronyltransferase 1 (glucuronosyltransferase P) B-cell CLL/lymphoma 3 Core-binding factor, runt domain, alpha subunit 2; translocated to, 2 Carbohydrate (N-acetylglucosamine 6-O) sulfotransferase 7 Dual specificity phosphatase 5 EMI domain containing 1 F-box protein 9 Histone cluster 2, H2ab Junctophilin 2 Jun B proto-oncogene Lectin, mannose-binding, 1 Uncharacterized LOC442075 Latent transforming growth factor beta binding protein 3 Melanoma antigen family D, 4B MicroRNA 101-1 Musashi RNA-binding protein 2 Metallothionein 1A Nuclear factor of kappa light polypeptide gene enhancer in B-cells 2 (p49/p100) Nucleolar protein 6 (RNA-associated) Nudix (nucleoside diphosphate linked moiety X)-type motif 4 Phosphatidylinositol glycan anchor biosynthesis, class M Phosphatidylinositol-5-phosphate 4-kinase, type II, alpha Protein phosphatase 1, regulatory subunit 3B Protein phosphatase 4, regulatory subunit 1 Ring finger protein 182 Ring finger protein 187 Reticulon 3 Sestrin 2 Serine/threonine kinase 40 Syntaxin binding protein 5 (tomosyn) Transducin (beta)-like 1X-linked Transcription elongation factor A (SII), 1 Thyroid hormone receptor, alpha Tribbles homolog 2 (Drosophila) ZFP36 ring finger protein-like 1 Zinc finger protein 777

Illumina ID

ILMN_1665442 ILMN_1911873 ILMN_1799860 ILMN_2152465 ILMN_1712236 ILMN_2345512 ILMN_3243112 ILMN_1707372 ILMN_2320906 ILMN_1751598 ILMN_2075927 ILMN_1684402 ILMN_1744795 ILMN_1698139 ILMN_1661683 ILMN_1714700 ILMN_1675448 ILMN_3235517

Gene

NOL6 NUDT4P1 PIGM PIP4K2A PPP1R3B PPP4R1 RNF182 RNF187 RTN3 SESN2 STK40 STXBP5 TBL1X TCEA1 THRA TRIB2 ZFP36L1 ZNF777

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Fold change

29 58 28 33 27 26 71 37 35 28 42 41 48 35 26 27 35 44

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Fig 3. Heat map of the differentially expressed genes generated using BRB-Array Tools. Genes were included if they had a greater than 2-fold difference in expression and were statistically significantly different, with a P-value of less than 0001 as determined by the univariate test yellow indicates upregulation, red indicates down-regulation. C indicates control, T indicates thalassaemic patient.

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Gene Expression in Thalassaemic Progenitor Cells

Gene ontology

given priority. Table SV details this breakdown alongside the fold change in expression. Based on the Q-ReTi-PCR validation, genes that were present in both analysis methods had the highest concordance with the microarray results (87%), followed by those present in BRB-ArrayTools only (80%) and lastly the bead level data (67%). No Q-ReTi-PCR results showed expression in the opposite direction compared to the microarray data. Interestingly, HBG1/2 and HBB showed no difference in gene expression in the microarray data, however when later assessed by Q-ReTi-PCR, while no statistical difference was observed for HBG1/2, HBB recorded a 78- (P = 00001) and 52- (P = 00000) fold down-regulation in the patients at day 7 and 14 compared to controls, which is consistent with the genotypes on the HbVar database. This divergence in HBB expression has been observed in other microarray studies.(Keller et al, 2006; Merryweather-Clarke et al, 2011) Of the confirmed differentially expressed genes, 80% showed a greater change in expression compared to the microarray results.

Using BRB-ArrayTools and DAVID for gene ontology analysis we identified apoptosis, MAPK signalling, nuclear factor (NF)-jB signalling and transcription factor activity pathways as being affected in the late erythroblasts of b-thalassaemia (Table III). Table SIV shows the top ten pathways from the KEGG and BioCarta databases. Examining gene lists in closer detail, genes with over 2-fold changes in expression for pathways of interest for apoptosis were ALS2, ATF5, BBC3, BCL2L1, BCL3, C5, CD70, CIDEC, CLN3, DAPK1, DDIT4, ERCC5, FGD2, FOSL2, IRAK2, JMY, MAP3K5, MMD, PHF17, PHLDA3, PRKACB, RASSF5, RTN3, SIAH2, TICAM1, VEGFA and XPA; for MAPK signalling were ARRB2, DUSP5, FOS, GNA12, MAPK1, MAP3K2, MAP3K3, MAP3K5, MAPKAPK2 and PRKACB; and for NF-jB signalling were AES, BCL2L1, BCL3, IRAK2, JUNB, NFKBIA, NFKBIZ, NFKB2, RELA, RELB, RTN3, STK40, TICAM1 and TNFAIP3.

Validation of results by quantitative real time PCR Using the two analysis methods described, two pools of data were obtained and created an overlapping list of 47 genes (Table II). A validation study was performed on 90 selected genes, of which 31 were observed by both analyses, 41 were identified from the BRB-ArrayTools analysis only and 18 were identified from bead level data only. Genes from pathways of interest observed from the gene ontology study were

Discussion The aim of this study was to examine the differences in gene expression in the erythroid progenitors of patients with b-thalassaemia major when compared to healthy controls, at two time points during their differentiation in vitro.

Table III. Integrated Gene Ontology (GO) analysis for pathways of interest using BRB Array Tools (B) and DAVID (D). Gene ontology term or pathway Transcriptional activity MAPK signalling pathway (Kegg: hsa04010) MAPKinase signalling pathway (BioCarta: h_mapkPathway) p38 MAPK signalling pathway (BioCarta: h_p38mapkPathway) NF-kB signalling pathway (BioCarta: h_nfkbPathway) Regulation of transcription, DNA-dependent NF-kappa-B/Rel/dorsal Positive regulation of gene expression Protein kinase cascade Basic-leucine zipper (bZIP) transcription factor Cell death Apoptosis (Kegg: hsa04210) Induction of apoptosis DNA damage response, signal transduction resulting in induction of apoptosis Cellular machinery Proteasome (Kegg: hsa03050) Phospholipid metabolic process Nucleosome Cell fraction

Analysis method

Number of genes

Number of GO terms

P-value

B B B B D D D D D

86 40 17 8 59 3 17 12 6

– – – – 20 8 17 32 7

000046 000035 004107 004149 0005 001 0027 0039 00015

B D D

38 15 5

– 12 6

004454 000072 00019

B D D D

9 9 6 27

– 9 31 6

000723 0012 00028 0015

P-values: BRB-Array Tools uses the LS permutation test (Simon, 2012) to find gene sets that have more genes differentially expressed among the phenotype classes than expected by chance. For DAVID, cluster of GO terms were first filtered for an enrichment score of >13 (statistically significant), P-values were obtained with a modified Fisher’s exact test (EASE score) (da Huang et al, 2009b). – indicates term not relevant for that analysis method. An enrichment score of >13 represents a statistically significant cluster. ª 2015 John Wiley & Sons Ltd British Journal of Haematology, 2015, 170, 257–267

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L. Forster et al After 7 d in culture we observed no difference in morphology or gene expression between the patient and control groups. A probable cause of this could be the pattern of globin production in erythroblasts. Globin production does not begin until the Pro-E/Baso-E stage of erythropoiesis and initially shows high HBG1/2 and low HBB expression, with this ratio inverting as maturation proceeds.(Pope et al, 2000; Ronzoni et al, 2008). As the Day 7 culture comprises almost exclusively proerythroblasts it seems likely that the increased HBG1/2 expression at this stage of differentiation masks the deleterious effects of reduced HBB expression in the beta thalassaemia group. Another contributing factor to the lack of differences in gene expression may be the cell culture conditions, where there is a fixed supply of growth factors and nutrients as opposed to a steady supply in vivo. In culture, cells expand from hundreds of HSCs to millions of late erythroblasts over the 14-day period. As such, the nutrients to cell ratio is dramatically higher earlier in the culture period. It has been previously demonstrated that nutrient feeding can provide protection from apoptosis and serum contains anti-apoptotic factors.(Zanghi et al, 1999). Stem cell factor stimulates proliferation of HSC and supports the growth of committed erythroid progenitors.(Broudy, 1997). EPO promotes the survival, proliferation and differentiation of erythroid progenitors both in vivo and in vitro.(Krantz, 1991) The SCF and EPO receptors are expressed strongly at the Pro-E stage, (Broudy et al, 1991) but their expression is downregulated and not detectable by the Poly-E stage. (Wickrema et al, 1992; Fisher et al, 1994; Testa et al, 1996). The gene expression and maturation patterns observed may be partly explained by the expression pattern of these receptors and strong proliferative effects of these growth factors. By Day 14 of culture there was a difference in the morphology when control and patient samples were compared, with a relative increase in Poly-E and reduced Ortho-E in the patient group. (Fig 1). This is similar to previously described bone marrow findings (Centis et al, 2000). Examining the variation in gene expression at day 14, we observed a clear difference between the patient and control groups (Fig 3). One patient sample (T2) was shown to lie within the patient group when compared with the control group; however this sample showed some differences in the gene expression levels when compared with samples from the other thalassaemia patients. This may correspond with the HBB genotypes of the different patients. Patient T2 was homozygous for the HBB:c.92 + 5G>C (IVS1-5G>C) mutation, whereas the other patients were either homozygous for the HBB:c.93-21G>A (IVS1-110G>A) mutation or compound heterozygous for this mutation and the HBB:c.118C>T, p.Gln40STOP (Codon 39 Gln>STOP) mutation. However, technical factors must also be considered as the total RNA yield for patient T2 at day 14 was lower, and there was a lower RNA to cell number ratio (data not shown). It was not 264

possible to obtain another sample from this patient so these results could not be confirmed. A subanalysis where the data for sample T2 was excluded did not greatly affect the identified differentially expressed genes in the microarray or Q-ReTi-PCR data, thus it was decided to retain this data in the overall analysis. In b-thalassaemia, premature death of erythroid progenitors by apoptosis is an accepted premise (Yuan et al, 1993; Schrier, 2002) and has been demonstrated at the Poly-E/ Ortho-E stage in cell culture.(Mathias et al, 2000) We therefore interrogated our data for differential expression of genes that may be involved in apoptosis. The majority of the identified genes were transcription factors indicated in apoptosis as opposed to genes directly involved in the apoptotic pathway such as the BCL2 family or those involved in the caspase cascade. Notable exceptions to this were BBC3, BAD, BID, BCL2L1 and CASP3. BBC3, BAD and BID are all proapoptotic BCL2 family members, while BCL2L1 splice variants can create pro- or anti-apoptotic proteins (Bcl-XS and Bcl-XL). These genes were all down regulated in the patient group. However only the proapoptotic BBC3, which codes for the BCL2 binding component 3 protein was downregulated by over 2-fold, the other genes were not. CASP3 was upregulated but again by less than 2-fold. BBC3, BCL2L1 and CASP3 were tested by Q-ReTi-PCR and, although the downregulation of BBC3 and BCL2L1 could be confirmed, the upregulation of CASP3 was not statistically significant. Smaller differences are more difficult to show as being statistically significant and these differences may have been borne out with a larger sample size. Interestingly, depriving CFU-E erythroblasts of EPO results in reduced Bcl-XL (encoded by BCL2L1) expression, no detection of the anti-apoptotic BCL2 and apoptosis by caspase three activation.(Gregoli & Bondurant, 1997). We detected no difference in BCL2 expression; expression levels for all samples were at or near baseline levels. It should be noted that splice variants for BCL2L1 have opposite apoptotic effects (Akgul et al, 2004) and these probes tested common regions. Therefore, while there are hints of apoptosis, a strong case for its presence was not observed at the gene expression level in our study. In our analysis the MAPK signalling pathway is strongly represented in gene ontology pathways. MAPK pathways can be split into three subfamilies, p38 (also termed MAPK14) kinases, Jun amino-terminal kinases and the extracellular signal-regulated kinases (ERK, also termed MAPK1).(Dhillon et al, 2007) MAPKs regulate the expression of many transcription factors and proteins of this pathway and play a critical role in regulation of cell growth, migration, proliferation, differentiation and survival.(Geest & Coffer, 2009). The MAPK pathway is involved when the erythroleukaemia K562 cell line is induced to undergo terminal erythroid differentiation.( Woessmann et al, 2004; Moosavi et al, 2007) Inhibition of induced mouse erythroleukaemia (MEL) cells with ERK and p38 pathway inhibitors resulted in an increase ª 2015 John Wiley & Sons Ltd British Journal of Haematology, 2015, 170, 257–267

Gene Expression in Thalassaemic Progenitor Cells in intracellular haem and a decrease in haemoglobin levels.(Mardini et al, 2010) A role for MAPK in erythroid precursors has previously been demonstrated through inhibition of MAPK phosphorylation, which resulted in decreased erythroid colony formation (Sui et al, 1998) and reduced Pro-E expansion.(Arcasoy & Jiang, 2005) Moreover, in-vitro Pro-E cells from b-thalassaemia/Hb E have been shown to have increased phosphorylation of MAPK.(Wannatung et al, 2009) We did not observe any corresponding changes at the gene expression level of our Pro-E samples; however, changes recorded in that study were post-transcriptional. This pathway is involved throughout erythropoiesis from HSC through to Ortho-E, (Uddin et al, 2004) with the p38 pathway shown to be important in enucleation of maturing erythroblasts.( Uddin et al, 2004; Schultze et al, 2012) The majority of genes identified in the MAPK pathway were down-regulated in the patient group, which may correlate with the observed delay in differentiation. The NF-jB family of transcription factors consists of five structurally related proteins namely, NF-jB1 (NFKB1; p50/ p105), NF-jB2 (NFKB2; p52/p100), RELA (p65), RELB and c-Rel (also termed REL).(Chen & Ghosh, 1999) All family members share the REL homology domain (RHD), a 300 amino acid region responsible for DNA binding. The interaction of these proteins with each other leads to the formation of active homo- and heterodimers, which, in turn, bind to jB sites on target genes, to regulate their expression.(Pahl, 1999) This pathway has previously been shown to be active in erythropoiesis with its gene expression being highest in Pro-E and gradually reducing as the cells mature.(Zhang et al, 1998) NF-jB proteins have been detected in mature RBCs with inhibition of the protein leading to eryptosis.(Ghashghaeinia et al, 2011) Other work has shown that EPO can prevent apoptosis through NF-jB translocation into the nucleus of erythroid progenitor cells, providing further evidence of a role in erythropoiesis.(Sae-Ung et al, 2005) In the day-14 samples, for thalassaemia patients we observed a decrease in expression of the NF-jB family members, and related genes including NFKB2, RELA, RELB, NFKBIA, NFKBIZ and BCL3 alongside a down regulation of BCL2L1. NF-jB directly activates promoter-binding sites on the antiapoptotic Bcl-X/BCL2L1.(Chen et al, 2000; Glasgow et al, 2000) There was also a decrease in NFKB1 but the difference was not significant. In addition TNFAIP3, also known as the zinc finger protein A20, which is regulated by NF-jB family proteins, was down-regulated in our patient group. This protein can protect against apoptosis but also promote necrosis while contributing to a NF-jB negative-feedback loop.(Malewicz et al, 2003; Storz et al, 2005) Moreover, NFjB binding motifs are present in the most highly expressed genes in reticulocytes at higher levels than when compared to all vertebrate promoters.(Goh et al, 2007). Combining our expression data with the work of other researchers discussed here, we believe that the NF-jB pathway plays an important ª 2015 John Wiley & Sons Ltd British Journal of Haematology, 2015, 170, 257–267

role in the pathogenesis of b-thalassaemia at the late erythroblast phase and justifies further study. In conclusion, this study has demonstrated a number of interesting findings. There is minimal difference between normal and thalassaemic erythroblasts at day 7 of culture, but there are significant differences at day 14 as maturation takes place and b globin synthesis increases. In addition to apoptosis, we have identified that the MAPK and NF-jB pathways are affected. As the latter pathways have an impact cell proliferation and differentiation, their role in the pathophysiology of b- thalassaemia major warrants further investigation.

Acknowledgements PathWest Laboratory Medicine WA for Financial support. Flow cytometry department at PathWest, Sir Charles Gairdner Hospital for assistance in setting up assays. Department of Research at Sir Charles Gairdner Hospital for statistical advice. Luke Forster’s PhD scholarship was provided by The University of Western Australia.

Author contributions Luke Forster: Conception and design, collection and/or assembly of data, data analysis and interpretation, manuscript writing. John McCooke: Data analysis and interpretation. Matthew Bellgard: Data analysis and interpretation. David Joske: Provision of study material and patients. Jill Finlayson: Conception and design, data analysis and interpretation critical revision of manuscript. Reza Ghassemifar: Conception and design, data interpretation and critical revision of manuscript.

Disclosure of potential conflict of interest The authors declare no competing financial interests.

Supporting Information Additional Supporting Information may be found in the online version of this article: Fig S1. Flow cytometry images of the post- CD71 (day 7) and CD235 (day 14) bead isolations for a control and thallasaemic patient. Fig S2. MDS plot showing similarities/differences between samples. Table SI. Gene information for 96 genes used for qPCR validation. Table SII. Complete list of up and downregulated genes in the patient group following 14 d in culture. Table SIII. Complete list of up and downregulated genes in the patient group following 14 d in culture. Table SIV. Gene ontology analysis using BRB-ArrayTools. Table SV. Genes used for qPCR validation defined by analysis method and pathway.

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Differential gene expression analysis in early and late erythroid progenitor cells in β-thalassaemia.

β- thalassaemia is a disorder of globin gene synthesis resulting in reduced or absent production of the β-globin chain in red blood cells. In this stu...
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