Digital gene expression profiling analysis of childhood acute lymphoblastic leukemia
- Authors:
- Published online on: April 5, 2016 https://doi.org/10.3892/mmr.2016.5089
- Pages: 4321-4328
Abstract
Introduction
Acute lymphoblastic leukemia (ALL) is the most commonly diagnosed malignancy in adolescents and young adults, and represents almost one third of all cases of cancer in children (1,2). ALL is a heterogeneous disease, and multiple subtypes have been identified based on recurrent copy number alterations and structural chromosomal rearrangements (3–5). Due to genetic variations, the incidence of ALL in children varies across regions, and is determined by ethnicity (1). In China, the incidence of childhood ALL is 4/100,000 children aged <10 years (6). Although the 5-year event-free survival rate of children and adolescents with ALL is ~80%, the remaining 20% of patients relapse, the outcome of which remains poor (7–9). Therefore, identifying ALL-associated differentially expressed genes is important for improving therapeutic methods and extending patient survival rates in childhood ALL.
Multiple technologies have been used to identify the differentially expressed genes associated with childhood ALL. For example, gene expression microarray analyses of differentially expressed genes in ALL subtypes (10) have identified 80-300 genes as marker genes, which are necessary for discriminating the subtypes. In addition, sequencing-based methods, including serial analysis of gene expression (SAGE), have been used to measure absolute gene expression levels in ALL subtypes (11). However, as hybridization-based methods are subject to technical limitations, certain genes may be overlooked during the hybridization process in gene expression microarray analysis. In addition, the cost of sequencing technology hinders the widespread usage of SAGE for identifying differentially expressed genes (12). Advances in second-generation DNA sequencing technologies has enabled digital gene expression (DGE) profiling to overcome the drawbacks of the microarray analysis hybridization process and allow the detection of differential expression of low-abundance transcripts on a genome-wide scale (13). In the present study, 3′ tag DGE, Gene Ontology (GO) analysis, and reverse transcription-quantitative polymerase chain reaction (RT-qPCR) were used to analyze the transcriptional profiles of bone marrow mononuclear cells (BMMCs) from Chinese children with ALL and those without ALL. The results of the analysis revealed numerous gene expression changes attributable to pathogenesis. By investigating changes in the expression of genes, various novel candidate genes required for ALL were identified.
Materials and methods
Patient samples
A total of 10 bone marrow (BM) tissue samples were obtained from children [median age at diagnosis, 8.1 years (range, 3–13.4 years); 5 males and 5 females] newly diagnosed with childhood ALL at Xiangya Hospital (Changsha, China) between May and August 2010. In addition, 10 non-ALL BM samples were obtained from patients who did not have leukemia or other malignancies, but who were undergoing BM aspiration as part of their clinical care at Xiangya Hospital [median follow-up time, 6.75 years (range 4–13.9 years)]. ALL diagnosis was based on the French-American-British classification standards, and the morphology, immunology, cytogenetics and molecular biology classification (14). Complete remission, refractory disease and BM relapse were defined, according to the National Cancer Institute (15). The patient clinical characteristics are listed in Table I. The primary BMMCs from the patients with ALL were analyzed using flow cytometry. The cell-surface antigen staining was performed using a PerCP-conjugated anti-CD45 antibody (cat. no. 304028; BioLegend, Inc., San Diego, CA, USA). For FACS analysis, 5×105 cells were acquired and scored using flow cytometer (Gallios™; Beckman Coulter, Inc., Brea, CA, USA), and the data were analyzed using FlowJo software (version 8.7; Tree Star, Inc., Ashland, OR, USA). The BMMCs were isolated by Ficoll density gradient centrifugation at 671 x g for 20 min at room temperature (Ficoll-Paque was obtained from GE Healthcare Life Sciences; Uppsala, Sweden). The cells at the interface were removed and washed twice with 30 ml sterile phosphate-buffered saline (Well-Biology Co., Ltd., Changsha, China) at 671 x g for 5 min. The isolated cells were counted using the trypan blue (Well-Biology Co., Ltd.,) exclusion method and an inverted phase contrast microscope (IMT-2; Olympus Corporation, Tokyo, Japan). The cells were pelleted and maintained at −80°C until RNA extraction. The RNA was extracted from frozen cell pellets using TRIzol reagent (Invitrogen; Thermo Fisher Scientific, Inc., Waltham, MA, USA), according to the manufacturer's protocol. In accordance with the Xiangya Hospital Committee on Human Research Review Ethics Committee, written informed consent was obtained from the patients, or from the parents or guardians, as appropriate.
Preparation of sequencing libraries and sequencing
The total RNA sample was digested using DNaseI (M0303S; New England Biolabs, Inc., Ipswich, MA, USA) and purified using oligo-dT beads (Invitrogen Dynabeads mRNA Purification kit; Thermo Fisher Scientific, Inc.), then poly(A)-containing mRNA was fragmented into 130 bp using First-Strand buffer (Thermo Fisher Scientific, Inc.). First-strand cDNA was generated using N6 primer, First Strand Master mix and Super Script II Reverse Transcriptase (Invitrogen; Thermo Fisher Scientific, Inc.) with the following reaction conditions: 25°C for 10 min, 42°C for 40 min and 70°C for 15 min. The Invitrogen Second Strand Master mix (Thermo Fisher Scientific, Inc.) was then added to synthesize the second-strand cDNA (at 16°C for 1 h). The cDNA was purified with Agencourt AMPure XP beads (Beckman Coulter, Inc.), combined with Invitrogen End Repair mix (Thermo Fisher Scientific, Inc.) and incubated at 20°C for 30 min. Invitrogen A-Tailing mix (Thermo Fisher Scientific, Inc.) was added, followed by incubation at 37°C for 30 min. The Adenylate 3′ Ends DNA (Agilent Technologies, Inc., Santa Clara, CA, USA), Illumina adapter (sequence, CATGIAAAAA; Illumina, Inc., San Diego, CA, USA) and ligation mix (Invitrogen; Thermo Fisher Scientific, Inc.) were combined and the ligate reaction was incubated at 20°C for 20 min. Fifteen rounds of PCR amplification were performed with PCR Primer Cocktail and PCR Master mix (both Takara Bio, Inc., Shiga, Japan) to enrich the cDNA fragments. Then the PCR products were purified with Agencourt AMPure XP beads. Sequencing libraries were prepared from 6 µg total RNA using the NlaIII Digital Gene Expression Tag Profiling kit (Illumina, Inc.). The bead-bound cDNA was digested with NlaIII and ligated with the Illumina adaptor (sequence, CATGIAAAAA) containing an MmeI recognition site. The adapter-ligated cDNA was digested with MmeI to release the cDNA from the bead, retaining a 17-bp sequence in the fragment. Following removal of the 3′ fragments via magnetic bead precipitation, a second Illumina adaptor was ligated to the 3′ end of the fragment to form a tagged library. Following 15 cycles of linear PCR amplification, 95-bp fragments were purified by 6% Tris-borate-EDTA polyacrylamide gel electrophoresis (Thermo Fisher Scientific, Inc.). Following denaturation, the single-chain molecules were adhered onto an Illumina Sequencing Chip (Illumina, Inc.). In situ amplification was performed to expand each molecule into a single-molecule cluster sequencing template. Subsequently, four color-labeled nucleotides (Thermo Fisher Scientific, Inc.) were added, and sequencing was performed using the sequencing by synthesis method (16,17). Each tunnel generated millions of raw reads with 35-bp sequencing lengths.
Reverse transcription-quantitative PCR (RT-qPCR) analysis
Total RNA was extracted from the BM using TRIzol reagent (Invitrogen; Thermo Fisher Scientific, Inc.), according to the manufacturer's protocol. The ensuing RT-qPCR was performed using an Access RT-PCR system (ISOGEN; Nippon Gene, Tokyo, Japan), according to the manufacturer's protocol. qPCR was performed using a 25-µl reaction mixture containing 12.5 µl 2X Premix Ex Taq™ (Takara Bio, Inc.), 5 pmol primer and 6.5 µl cDNA, obtained as described above. Amplification was performed on an Applied Biosystem PRISM 7700 system (Thermo Fisher Scientific, Inc.) and the PCR conditions were as follows: 50°C for 2 min, 95°C for 15 min, and 45 cycles at 95°C for 30 sec and 60°C for 1 min, followed by 25°C for 2 min. Quantification was determined by the standard curve and the 2−ΔΔCq method (4). All primer sets were designed, as described previously, synthesized by Biosearch Technologies (Novato, CA, USA) and are presented in Table II.
Gene annotation
The human transcriptome (Ensembl version 58; http://asia.ensembl.org/) was used as the reference sequence for sequence read alignment and identification. The DGE tags were annotated by mapping the reads to the sequence-flanking NlaIII restriction sites on the coding strands. Alignment and candidate gene identification were performed, as reported previously (18). The total expression profile for each gene was then calculated by summing all the tags mapped to the same gene, including intronic tags.
Statistical analysis
Data were analyzed using GraphPad Prism Software (version 5.0; GraphPad Software, Inc, San Diego, CA, USA). Data are presented as means ± standard deviation or means ± standard error of the mean. Statistical analyses were performed using two-tailed Student's t-tests for the differences between two groups and two-way analysis of variance for the difference between multiple groups. P<0.05 was considered to indicate a statistically significant difference.
Results
mRNA sample preparation and Illumina genome analyses
In the present study, BMMC samples were collected from pediatric patients with ALL (Table I) and from non-ALL individuals. The cellular mRNA was extracted and DGE sequencing libraries were prepared for sequencing in an Illumina Genome Analyzer, which obtained 15,200,000–20,400,000 quality-filtered sequence reads (tags) per sample. Any tags with an abundance of <2 tags per million (TPM), those that mapped to >1 gene, or those that did not match the Ensembl database reference sequence were omitted. Following filtering, 25,000–53,000 unique nucleotide sequence tags were obtained per library, which were mapped to the transcriptome (Fig. 1).
Identification and functional classification of differentially expressed genes
The differentially expressed genes were compared between the patients with childhood ALL and the non-ALL individuals. The selection criteria for putative differentially expressed genes were as follows: i) Average fold change between ALL and non-ALL groups ≥2; ii) single sample t-test false discovery rate <0.1%. In the ALL group, 2,825 genes were upregulated and 1,952 genes were down-regulated (Fig. 2). Of the upregulated genes, 37 that have been associated with the promotion of tumorigenesis, according to Ensembl, were upregulated >10-fold in childhood ALL (Table III).
GO analysis
In the present study, GO analysis was performed by mapping each differentially expressed gene into the GO database (http://www.geneontology.org/). The predominant functional group of upregulated genes was associated with the positive regulation of the cellular process, whereas the down-regulated genes were associated with multiple function groups involved in immune cell differentiation, the metabolic process and programmed cell death (Fig. 3).
RT-qPCR analysis of differentially expressed genes
To further evaluate the DGE profiling reliability in the present study, RT-qPCR analysis was performed for a subset of seven genes, which had been determined by the DGE profiling as upregulated >10-fold in the ALL group. The seven genes were expressed at high levels in the ALL samples, and had the same expression profiles as in the initial DGE profiling (Fig. 4). This result confirmed the reliability of the DGE profiling.
Discussion
ALL is a multigenic disease with multiple subtypes; recurrent copy number alterations and structural chromosomal rearrangements characterize the disease. Although the clinical perspective for the ALL subtypes is well established, the underlying molecular mechanisms in the development of ALL development remain to be fully elucidated Thus, char-acterizing ALL-associated differentially expressed genes is important for determining the molecular mechanisms of ALL and for early ALL diagnosis. Several genomic-wide expression profiling approaches have been used to identify the ALL-associated differentially expressed genes (19). The development of second-generation sequencing has enabled the use of DGE profiling for determining genome-wide gene expression profiles in ALL cell samples. For example, Nordlund et al used DGE profiling to characterize the gene expression patterns in different ALL subtypes (20), and found that antisense tags expressed from the non-coding strand were also expressed as a subtype-specific pattern. Additionally, other studies have used microarray-based methods to profile ALL gene expression patterns (10,11,21) However, these previous studies were performed in Western countries, and comparable information in Asian populations is limited. To the best of our knowledge, the present study is the first to use DGE profiling to determine gene expression patterns in childhood ALL in a Chinese population.
The development of second-generation sequencing technology has resulted in DGE profiling possessing several advantages, compared with earlier methods of genome-wide expression analysis (22) For example, it requires smaller RNA samples and costs less than other transcriptome sequencing methods, and it overcomes the limitations of the hybridization process present in microarray-based methods. In addition, computational calculation analysis of DGE data is less challenging. In the present study, DGE profiling revealed that 37 genes were upregulated by >10-fold in the childhood ALL group, compared with the non-ALL group. Notably, these genes are important in tumorigenesis. For example, the wild-type WT1 is expressed in breast cancer, renal cell cancer, ovarian cancer, mesothelioma, lung cancer, melanoma and acute leukemia (23,24). and high levels of WT1 are associated with poor prognosis in ovarian cancer and leukemia (25,26) Other genes, including HOXA11, HHIP, NCR3 and ERBB2 are involved in the tumorigenesis of several types of solid tumor (27–31). Thus, in addition to characterizing the expression patterns of ALL-associated genes in the present study, novel candidate genes have been identified, which may be associated with ALL. In addition, GO analysis demonstrated that these genes were predominantly involved in immune cell differentiation, the metabolic process and programmed cell death, suggesting the importance of these signaling pathways in ALL. Future investigations to characterize the roles of these genes in childhood ALL experimentally may further understanding.
In conclusion, DGE profiling was conducted to determine the gene expression profile of childhood ALL in a Chinese population, identifying 2,825 upregulated and 1,952 down-regulated genes. Of these, 37 of the upregulated genes were upregulated by >10-fold, and were found to be important in tumorigenesis. These findings suggested that DGE profiling can provide novel genome-wide information on gene expression in ALL.
Acknowledgments
The present study was supported by grants from The National Natural Sciences Foundation of China (grant nos. 31171328 and 81370648 to Professor Li-Zhi Cao, 81270606 to Dr Yan Yu, 81100359 to Dr Ming-Hua Yang and 81400132 to Dr Shan Zhu) and the Natural Science Foundation of Hunan Province, China (grant no. 2015JJ6118).
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