Open Access

Retrospective screening of microarray data to identify candidate IFN-inducible genes in a HTLV-1 transformed model

  • Authors:
    • Alaa Refaat
    • Mohamed Owis
    • Sherif Abdelhamed
    • Ikuo Saiki
    • Hiroaki Sakurai
  • View Affiliations

  • Published online on: February 9, 2018     https://doi.org/10.3892/ol.2018.8014
  • Pages:4753-4758
  • Copyright: © Refaat et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

HuT-102 cells are considered one of the most representable human T-lymphotropic virus 1 (HTLV-1)-infected cell lines for studying adult T‑cell lymphoma (ATL). In our previous studies, genome‑wide screening was performed using the GeneChip system with Human Genome Array U133 Plus 2.0 for transforming growth factor‑β‑activated kinase 1 (TAK1)‑, interferon regulatory factor 3 (IRF3)‑ and IRF4‑regulated genes to demonstrate the effects of interferon‑inducible genes in HuT‑102 cells. Our previous findings demonstrated that TAK1 induced interferon inducible genes via an IRF3‑dependent pathway and that IRF4 has a counteracting effect. As our previous data was performed by manual selection of common interferon‑related genes mentioned in the literature, there has been some obscure genes that have not been considered. In an attempt to maximize the outcome of those microarrays, the present study reanalyzed the data collected in previous studies through a set of computational rules implemented using ‘R’ software, to identify important candidate genes that have been missed in the previous two studies. The final list obtained consisted of ten genes that are highly recommend as potential candidate for therapies targeting the HTLV‑1 infected cancer cells. Those genes are ATM, CFTR, MUC4, PARP14, QK1, UBR2, CLEC7A (Dectin‑1), L3MBTL, SEC24D and TMEM140. Notably, PARP14 has gained increased attention as a promising target in cancer cells.

Introduction

In 1977, a group of scientists from Japan identified adult T-cell lymphoma (ATL) as a helper T-cell malignancy (1). A year later, HuT-102 cells were developed from a patient with cutaneous T-cell Lymphoma (2). HuT-102 cells were identified as a subset of helper T-cells with high expression of IL-2 receptor and constitutive IL-2 production, aiding in an autonomous proliferation (2). In 1980, the human T-lymphotropic virus 1 (HTLV-1) was identified in HuT-102 cells, followed by its discovery in cells from ATL patient. Analysis of serum samples from around the world provided information of endemic areas for HTLV-1, and its role as an etiologic agent for ATL (1).

Similar to freshly obtained cells from ATL patients, HuT-102 cells is a helper T-cell cell that express Tax protein, which promote these cells particularly as an excellent model for studying ATL (3). Our previous findings have identified the interferon regulatory factor (IRF) family members as efficient mediators that modulate cytokines profile in those cells, hence providing therapeutic solutions (3). Although the microarray data upon which we based our previous studies were used to elaborately cover the roles of IRFs, particularly IRF3 and IRF4, on Interferon-inducible genes, it was performed by manual selection of related genes mentioned in the literature (3,4). Therefore, we try in the current study to reanalyze the previous data through more efficient computational tools. The aim of browsing our previous data for further analysis is to find important genes that we might have passed away, in order to discover promising opportunities against ATL.

Materials and methods

Gene expression was analyzed using a GeneChip system with Human Genome Array U133 Plus2.0 (Affymetrix; Thermo Fisher Scientific, Inc., Waltham, MA, USA) as described previously (3,4). The arrays were performed independently in two studies as follow, a total of four arrays for the first study: Two for HuT102-shLuc cells and two for HuT102-shTAK1 cells (deposited in the GEO database under accession no. GSE16219). In the second study, six arrays were used: Two for HuT102-siLuc cells, two for HuT102-siIRF4 cells, and two for HuT102-siIRF3 cells (deposited in the GEO Database under accession no. 22036). The findings of both studies are summarized in Fig. 1. In order to better analyze our study, we restricted the number of the analyzed genes. Our criteria was to focus on the genes which have markedly changed. We primarily excluded genes with values between 0.60 and 1.5 fold changes. Genes equal to or less than 0.60 are considered downregulated, and genes equal to or more than 1.50 are considered upregulated. The number of genes remaining after this step was 9,302, 1,343, and 1,447 genes for TAK1, IRF3, and IRF4 arrays, respectively. Next, we used GeneVenn (genevenn.sourceforge.net) to generate a Venn chart demonstrating the set of common genes between the different sets of arrays. The Venn chart indicated a list of 706 common genes between TAK1 and IRF3, 751 common genes between TAK1 and IRF4, 633 common genes between IRF3 and IRF4, and finally 346 common genes between the three datasets. Finally, R software for statistical computing of data sets was used, as explained below, to restrict our selection to the most promising genes, as indicated.

Results

Rules applied on the identified datasets

In this study, we focused on IRF4 compared to both TAK1 and IRF3, or IRF3 only. Therefore, we continued our analysis using either the 346 common genes between the three datasets (Set A), or the 633 common genes between IRF3 and IRF4 (Set B) (Fig. 2). To filter down the list of genes generated from the Venn chart, we applied simple rules for the inclusion of genes. We firstly denoted the basic level of a given gene as the average of luciferase transfected sample in the given arrays. We then identified the basic level with different class categories, and applied rules of inclusion for genes according to Tables I and II. Through the applied criteria, we obtained 85 genes out of Set A, and 108 genes out of Set B (Figs. 3 and 4). The next stage was to identify genes inversely correlated between IRF4 against both TAK1 and IRF3, and IRF4 against IRF3 (Fig. 4). From this step, we obtained 14 genes from Set A, and 16 genes from Set B. As our goal from this analysis is to identify new genes inversely controlled by the IRF4 axis vs. the TAK1-IRF3 axis, we excluded unspecified genes, and genes related to interferon for being already covered in previous studies (3,4) (Tables III and IV, and Fig. 4).

Table I.

Rules implemented for set A (IRF4#IRF3/TAK1) and the perspective number of genes.

Table I.

Rules implemented for set A (IRF4#IRF3/TAK1) and the perspective number of genes.

Basic levelClassNo. of genesRuleNo. of genes after applying the rule
>1005  13Include all13
50–1004  18Include if 2 arrays are below 0.5 or more than 215
25–503  94Include if 2 arrays are below 0.4 or more than 432
10–252132Include if 2 arrays are below 0.3 or more than 621
0–101  89Include if 2 arrays are below 0.2 or more than 8  4
Total 346 85

[i] TAK1, transforming growth factor-β-activated kinase 1; IRF, interferon regulatory factor.

Table II.

Rules implemented for set B (IRF4#IRF3) and the perspective number of genes.

Table II.

Rules implemented for set B (IRF4#IRF3) and the perspective number of genes.

Basic levelClassNo. of genesRuleNo. of genes after applying the rule
>1005  13Include all  13
50–1004  72Include if 2 arrays are below 0.5 or more than 2  49
25–503186Include if 2 arrays are below 0.4 or more than 4  38
10–252201Include if 2 arrays are below 0.3 or more than 6     8
0–101161Include if 2 arrays are below 0.2 or more than 8     0
Total 633 108

[i] IRF, interferon regulatory factor

Table III.

Final list of inversely correlated genes created from set A (IRF4#IRF3/TAK1).

Table III.

Final list of inversely correlated genes created from set A (IRF4#IRF3/TAK1).

Gene symbolGene nameArray 1 (TAK1/luc)Array 2 (IRF3/luc)Array 3 (IRF4/luc)Class
ATMAtaxia telangiectasia mutated0.520.2612.872
CFTRCystic fibrosis transmembrane conductance regulator4.312.110.343
CXCL10Chemokine (C-X-C Motif) ligand 100.350.196.555
CXCL11Chemokine (C-X-C Motif) ligand 110.210.198.985
FLJ10213Hypothetical protein LOC550960.580.164.233
GBP4Guanylate binding protein 40.510.457.514
IFIT1Interferon-induced protein with tetratricopeptide repeats 10.360.335.275
IFIT2Interferon-induced protein with tetratricopeptide repeats 20.50.292.495
IFIT3Interferon-induced protein with tetratricopeptide repeats 30.480.432.235
IL18RAPInterleukin 18 receptor accessory protein0.220.492.215
MUC4Mucin 4, cell surface associated1.663.290.313
PARP14Poly (ADP-ribose) polymerase family, member 140.10.582.945
QKIQKI, KH domain containing, RNA binding4.385.40.592
UBR2Ubiquitin protein ligase E3 component N-recognin 20.290.231.942

[i] TAK1, transforming growth factor-β-activated kinase 1; IRF, interferon regulatory factor.

Table IV.

Final list of inversely correlated genes created from set B (IRF4#IRF3).

Table IV.

Final list of inversely correlated genes created from set B (IRF4#IRF3).

Gene symbolGene nameFold 1 (IRF3/luc)Fold 2 (IRF4/luc)Class
ATMAtaxia telangiectasia mutated0.2612.873
CLEC7AC-type lectin domain family 7, member A0.144.622
CXCL10Chemokine (C-X-C Motif) ligand 100.196.555
CXCL11Chemokine (C-X-C Motif) ligand 110.198.985
FLJ10213(unidentified)0.164.233
GBP4Guanylate binding protein 40.457.514
IFIT1Interferon-induced protein with tetratricopeptide repeats 10.335.275
IFIT2Interferon-induced protein with tetratricopeptide repeats 20.292.495
IFIT3Interferon-induced protein with tetratricopeptide repeats 30.432.235
IL18RAPInterleukin 18 receptor accessory protein0.492.215
ISG20Interferon stimulated exonuclease gene 20 kDa0.452.245
L3MBTLLethal(3)malignant brain tumor-like gene0.134.843
LOC729397Hypothetical LOC7293970.312.234
PMLPromyelocytic leukemia0.532.065
SEC24DSEC24 family member D0.313.243
TMEM140Transmembrane protein 1400.352.614

[i] IRF, interferon regulatory factor.

List of genes identified as possible future therapeutic targets

The final list we obtained consists of 10 genes that we highly recommend as potential candidate for therapies targeting the HTLV-1 infected cancer cells, and might be potential in other cancers as well.

i) Ataxia telangiectasia mutated (ATM) is a Serine/threonine protein kinase with a distinct role in double-strand breaks. Its activation causes the phosphorylation and the activation of several downstream DNA damage and cell cycle arrest checkpoints including the histone variant H2ax, the effector protein kinases, and the tumor suppressor p53 (57). Germ-line mutations of ATM lead to ataxia-telangiectasia and shows a high risk of breast cancer (8).

ii) Cystic Fibrosis Transmembrane conductance Regulator (CFTR) is Chloride channel membrane protein (9). It is a member of the ABC transporter superfamily and is activated by phosphorylation by PKA. CFTR conducts anions to flow down their electrochemical gradient and facilitate the passive movement of the positively charged ions. The CFTR gene has been reported to be mutated in patients with cystic fibrosis (CF) and has been a potential therapeutic target. In 2012, the FDA approved Ivacaftor as the first targeted therapy for patient with CF (10). Recently CFTR was identified as s a tumor suppressor including intestinal, prostate and ovarian cancer (1113). Moreover, CFTR was shown as a potent suppressor of epithelial-to-mesenchymal transition (EMT) breast cancer cells and was associated with poor prognosis in patients (14).

iii) Mucin 4 (MUC4) is a high molecular weight glycosylated protein, which plays various roles in promoting cancer progression (15). Overexpression of mucins has been shown to reduce of cell adhesion and promote cancer cell migration and metastasis. MUC4 also phosphorylate the ErbB2 leading to increasing the tumor cell proliferation independent on the activation of neither the MAPK nor AKT pathways. Importantly, MUC4 has been shown as a promising biomarker for diagnoses of pancreatic cancer with undetectable levels in normal pancreas (16).

iv) Poly (ADP-ribose) polymerase 14 (PARP14) is a member of the PARP family of proteins, which involves in DNA repair and programmed cell death. PARP14 was shown to regulate the STAT6-dependent transcription (17). Moreover, Iansante et al has reported the vital role of PARP14 to mechanistically link apoptosis to metabolism. Through inhibiting the JNK1/PKMa2 regulatory axis, PARP14 potentially promoted Warburg effect in hepatocellular carcinoma (18).

v) Quaking homolog, KH domain RNA binding (QK1) is a RNA-binding protein. It is a member of the Signal Transduction and Activation of RNA (STAR) proteins family. QK1 was reported to play a distinct role in schizophrenia via regulating the myelin-related genes activity (19). QKI has been recently shown to be downregulated in lung cancer resulted in poorer prognosis (20). Additionally, it suppresses glioblastoma by stabilizing microRNA-20a leading to regulating TGFβ pathway (21).

vi) UBR2 is an E3 ubiquitin-protein ligase, which acts by recognizing and binding to the N-terminal residues-carrying proteins leading to their ubiquitination and degradation. Notably, UBR2 together with UBR1 were shown as potential negative regulators of mammalian target of rapamycin (mTOR) pathway via degrading the Leucine protein, and therefore its expression can be associated with cancer elimination (22).

vii) C-type lectin domain family 7 member A (CLEC7A, also known as Dectin-1) is a glycoprotein with a distinct role in regulating innate immunity. It is predominantly expressed in macrophages, neutrophils and dendritic cells and binds both CD4+ and CD8+ T cells (23). Dectin-1 also recognizes several fungal species, which triggers the induction of numerous cytokines and chemokines including TNF-α, IL-2, IL-6, and IL-23 (24). Mechanistically, Dectin-1 ligand has been shown to act via recruiting and activating the NFkB inflammatory pathway (25). Importantly, activation of dectin-1 on macrophages induces an adaptive immune suppression and promotes pancreatic cancer progression (26).

viii) Lethal (3) malignant brain tumor-like protein (L3MBTL) is a polycomb group protein (PcG) that recognizes and binds methyllysine residues on the target proteins leading to post-translational modifications. L3MBTL was shown to be associated with myeloid malignancies (27).

ix) SEC24D is a member of the SEC23/SEC24 family, which is involved in vesicle trafficking at the endoplasmic reticulum (28).

x) TMEM140 is the transmembrane protein 140, shown as a promising prognostic marker for patients with glioma where its overexpression strongly correlates with tumor size and overall patients' survival rates. Importantly, silencing TMEM140 suppressed the viability, migration, and invasion of glioma cells suggesting its importance as an attractive therapeutic target (29).

Discussion

In this study, we have identified a set of 10 genes relevant to interferon signaling. To date, interferon based therapy is widely used to treat ATL. Some of the identified genes might have wider scope of functions irrespective to the interferon-related signaling, per se. With the exception of SEC24D, which has very limited data, the remaining genes in the list has several roles in cancer progression through their overexpression or mutation. As a matter of interest, PARP-14 is of an exceptionally rising importance in cancer research and metabolism. PARP-14 and other molecular targets identified in this study can act as potential therapeutic targets for Cancer (18,30).

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April 2018
Volume 15 Issue 4

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APA
Refaat, A., Owis, M., Abdelhamed, S., Saiki, I., & Sakurai, H. (2018). Retrospective screening of microarray data to identify candidate IFN-inducible genes in a HTLV-1 transformed model. Oncology Letters, 15, 4753-4758. https://doi.org/10.3892/ol.2018.8014
MLA
Refaat, A., Owis, M., Abdelhamed, S., Saiki, I., Sakurai, H."Retrospective screening of microarray data to identify candidate IFN-inducible genes in a HTLV-1 transformed model". Oncology Letters 15.4 (2018): 4753-4758.
Chicago
Refaat, A., Owis, M., Abdelhamed, S., Saiki, I., Sakurai, H."Retrospective screening of microarray data to identify candidate IFN-inducible genes in a HTLV-1 transformed model". Oncology Letters 15, no. 4 (2018): 4753-4758. https://doi.org/10.3892/ol.2018.8014