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. 2023 Jul 10;18(7):e0288031. doi: 10.1371/journal.pone.0288031

Establishment of the lymphoid ETS-code reveals deregulated ETS genes in Hodgkin lymphoma

Stefan Nagel 1,*, Corinna Meyer 1, Claudia Pommerenke 1
Editor: Klaus Roemer2
PMCID: PMC10332584  PMID: 37428779

Abstract

The human family of ETS transcription factors numbers 28 genes which control multiple aspects of development, notably the differentiation of blood and immune cells. Otherwise, aberrant expression of ETS genes is reportedly involved in forming leukemia and lymphoma. Here, we comprehensively mapped ETS gene activities in early hematopoiesis, lymphopoiesis and all mature types of lymphocytes using public datasets. We have termed the generated gene expression pattern lymphoid ETS-code. This code enabled identification of deregulated ETS genes in patients with lymphoid malignancies, revealing 12 aberrantly expressed members in Hodgkin lymphoma (HL). For one of these, ETS gene ETV3, expression in stem and progenitor cells in addition to that in developing and mature T-cells was mapped together with downregulation in B-cell differentiation. In contrast, subsets of HL patients aberrantly overexpressed ETV3, indicating oncogenic activity in this B-cell malignancy. Analysis of ETV3-overexpressing HL cell line SUP-HD1 demonstrated genomic duplication of the ETV3 locus at 1q23, GATA3 as mutual activator, and suppressed BMP-signalling as mutual downstream effect. Additional examination of the neighboring ETS genes ETS1 and FLI1 revealed physiological activities in B-cell development and aberrant downregulation in HL patient subsets. SUP-HD1 showed genomic loss on chromosome 11, del(11)(q22q25), targeting both ETS1 and FLI1, underlying their downregulation. Furthermore, in the same cell line we identified PBX1-mediated overexpression of RIOK2 which inhibited ETS1 and activated JAK2 expression. Collectively, we codified normal ETS gene activities in lymphopoiesis and identified oncogenic ETS members in HL.

Introduction

In the course of hematopoiesis, production of the different types of blood and im-mune cells follows a tree-like system. Hematopoietic stem cells (HSCs) residing in the bone marrow, generate common lymphoid and common myeloid progenitors (CLPs and CMPs), the starting points for lymphopoiesis and myelopoiesis, respectively. The lymphoid lineage produces B-cells, T-cells, NK-cells and innate lymphoid cells (ILCs). In contrast to NK-cells and ILCs, differentiation of B-cells and T-cells terminates outside the bone marrow in lymph nodes and the thymus, respectively. All these differentiation steps are controlled at the transcriptional level [1,2]. Therefore, understanding normal and abnormal regulation of hematopoiesis requires knowledge of transcription factors (TFs), representing the main players.

The human genome encodes about 1600 TFs [3]. These are classified according to similarities in sequence and structure of their DNA-binding domains. Important TF families controlling hematopoiesis include basic helix-loop-helix, ETS, homeodomain, T-box, and Zinc-finger factors [4]. For comprehensive descriptions of active TFs in the hematopoietic compartment, we have mapped selected TF families to generate so-called TF-codes. According to that approach, we have reported the NKL-code for NKL homeobox genes, the TALE-code for TALE-class homeobox genes, and the TBX-code for T-box genes [5–8]. By describing physiological conditions of TFs activated in all stages of hematopoiesis, these codes enable deregulated TF-encoding genes in lymphoid and myeloid malignancies to be identified and assessed.

In this study, we focused on the ETS family of TFs [9,10]. Their nomenclature derives from an avian erythroblastosis virus which carries the oncogene E-twenty-six [11]. The human genome encodes 28 ETS factors. These share the about 85 amino acids long ETS domain which consists of three alpha-helices and a four-stranded antiparallel beta-sheet scaffold, and thus belongs to the winged helix-turn-helix group of TFs [12,13]. Differences in ETS domain sequences allow classification of these factors into nine subfamilies [10]. Functionally, this domain performs both DNA-binding and protein-interaction [14]. DNA-sequences recognized by particular ETS factors show only limited differences, indicating that these TFs control specific gene activities in concert with cofactors [15,16].

Several ETS factors are reportedly involved in regulation of hematopoietic devel-opment, including ETS1, FLI1, ETV6/TEL, SPI1/PU.1, and SPIB [17]. ETS1, for example, interacts with master factor PAX5 to regulate B-cell specific target genes [18,19]. On the other hand, mutated or deregulated ETS factors have been described in cancer, notably leukemia and lymphoma [20]. ETV6, for example, is part of diverse oncogenic fusion genes in lymphoid and myeloid malignancies while FLI1 operates context-dependently either as oncogene or tumor suppressor gene [20,21].

Over the years, classification of lymphoid neoplasms has been steadily modified and recently revised [22]. According to this system, Hodgkin lymphoma (HL) is a B-cell malignancy separated into classical HL and nodular lymphocyte predominant HL (NLPHL). Pathologically, HL tumor cells share aberrant survival by activation of NFkB-signalling while downregulation of several master factors controlling B-cell differentiation, including PAX5 and EBF1 is restricted to classical HL [23–25].

Here, we have generated the lymphoid ETS-code, delineating physiological activities of ETS genes in lymphopoiesis. Subsequent evaluation of ETS genes using public expression data from HL patients revealed several deregulated members including ETV3, ETS1 and FLI1 analyzed in more detail using HL cell lines as models.

Results

Establishment of the lymphoid ETS-code

By exploiting several public datasets containing gene expression profiling or RNA-sequencing data, we have mapped the activities of all 28 ETS genes in normal hu-man hematopoietic entities. This approach corresponded with previously reported screenings [5–8]. We analyzed datasets for early hematopoiesis including stem and progenitor cells, for developing and mature T-cells, B-cells, and ILCs, in addition to comprehensive RNA-seq data covering a variety of cells from several normal hematopoietic lineages (S1–S4 Figs). The combined results are given in Fig 1, representing the so-called lymphoid ETS-code. According to this code, we detected 19 ETS genes expressed in early hematopoiesis and lymphopoiesis, including ELF1, ELF2, ELF4, ELK1, ELK3, ELK4, ERF, ERG, ETS1, ETS2, ETV3, ETV5, ETV6, ETV7, FLI1, GABPA, SPI1, SPIB and SPIC, ranging from 3 to 17 genes per entity.

Fig 1. Graphical representation of the lymphoid ETS-code, showing ETS gene actvities in entities from early hematopoiesis and lymphopoiesis.

Fig 1

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This code was generated using public datasets. Three ETS genes are highlighted: ETV3 in red, and ETS1 and FLI1 in blue. Abbreviations: BCP: B-cell progenitor, CILP: Common innate lymphoid progenitor, CLP: Common lymphoid progenitor, CMP: Common myeloid progenitor, DN: Double negative T-cell, DP: Double positive T-cell, ETP: Early T-cell progenitor, GC: Germinal center, HSC: Hematopoietic stem cell, ILC: Innate lymphoid cell, ILCP: Innate lymphoid cell progenitor, LMPP: Lymphoid and myeloid primed progenitor, memo: Memory, NKP: NK-cell progenitor.

Inspection of their activities revealed interesting assignments: ELF1 and ETS1 were active in all entities, GABPA remained silent in NK-cells only, and ERF was expressed ubiquitously except in ILCs. ETV5 was active in progenitors and silent in mature cells, while SPIC expression was restricted to NK-cells. ERG was expressed in progenitor cells and mature NK-cells but downregulated in B-cells, T-cells, and ILCs. Finally, ELK1 showed activity in all entities except NK-cells and ILCs. Looking at developing B-cells, the data indicated that ETV7 was active in plasma cells but silent in memory B-cells while SPIB was expressed in memory B-cells but repressed in plasma cells. ETV3 was found to be downregulated in the development of B-cell while still active in that of T-cells. Alltogether, this code showed several conspicuous gene expression patterns, probably underlying specific differentiation processes or the maintenance of immune cell identities.

Deregulated ETS genes in Hodgkin lymphoma

The established lymphoid ETS-code assists identification of deregulated ETS genes in lymphoid malignancies. Here, we analyzed HL using the public gene expression profiling dataset GSE12453 which contains 17 HL patients in addition to other types of lymphoma, and normal developing B-cells, serving as controls (S5 Fig). According to this approach, we compended deregulated ETS genes in HL patients as shown in Table 1, detecting seven aberrantly upregulated (EHF, ELK1, ETS2, ETV3, ETV6, ETV7, SPIC) and five downregulated ETS genes (ELF1, ELF2, ELK3, ETS1, FLI1). Normally, ETV3 remained active in T-cell development while downregulated in the development of B-cells and ILCs, and the activity of SPIC was restricted to NK-cells. Thus, aberrant expression of these genes may disturb processes of normal B-cell differentiation by reactivating programs appropriated from other lineages, or by stopping the normal B-cell program. ETS1 and FLI1 were widely expressed in lymphopoiesis. Their aberrant downregulation may, therefore, affect rather basic aspects of lymphoid differentiation.

Table 1. Aberrant ETS gene activities in HL patients and cell lines.

gene ID HL patients
(GSE12453)		 HL cell lines
(LL-100: E-MTAB-7721)
EHF 225645_at up L-428
ELF1 212418_at down L-428, L-1236, SUP-HD1
ELF2 203822_s_at down SUP-HD1
ELF3 210827_s_at
ELF4 31845_at
ELF5 220625_s_at
ELK1 203617_x_at up ---
ELK3 221773_at down L-428, L-1236, SUP-HD1
ELK4 205994_at
ERF 203643_at
ERG 213541_s_at
ETS1 224833_at down SUP-HD1
ETS2 201328_at up SUP-HD1
ETV1 206501_x_at
ETV2 215510_at
ETV3 1552423_at up HDLM-2, KM-H2, L-1236, SUP-HD1
ETV3L - ?
ETV4 1554576_a_at
ETV5 203349_s_at
ETV6 235056_at up HDLM-2
ETV7 221680_s_at up HDLM-2
FEV 207260_at
FLI1 204236_at down SUP-HD1
GABPA 210188_at
SPDEF 213441_x_at
SPI1 205312_at
SPIB 205861_at
SPIC 1553851_at up DEV
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Cell lines are suitable models to analyze regulation and function of genes in the context of their derived malignancy [26]. Therefore, we screened the LL-100 RNA-seq dataset (E-MTAB-7721) containing 100 hematopoietic cell lines derived from myeloid and lymphoid types of leukemia and lymphoma, including HL [27]. HL cell lines showing overexpression or downregulation of those ETS genes identified in HL patients were included in Table 1 (S6 Fig). Thus, we found for each ETS gene deregulated in HL patients one or more corresponding HL cell lines to serve as models for detailed analyses.

ETV3 in Hodgkin lymphoma

In the following, we focused on ETS gene ETV3, downregulated in normal B-cell development (Fig 1) and aberrantly activated in 4/17 (23%) HL patients of dataset GSE12453 including classical HL and NLPHL, and in 2/29 (7%) classical HL patients of dataset GSE39134 (Fig 2A). The LL-100 RNA-seq dataset showed elevated ETV3 expression in 4/6 HL cell lines and additionally in 4/4 cell lines derived from anaplastic large cell lymphoma (ALCL) (Fig 2B). This observation may be of interest because HL and ALCL share some pathological features [28]. RQ-PCR and Western blot analysis confirmed peak ETV3 expression levels in HL cell lines HDLM-2 and SUP-HD1 (Fig 2C and 2D). Of note, elevated protein expression levels in HDLM-2 may indicate additional post-transcriptional regulation. Furthermore, ETV3 expression in SUP-HD1 was higher when compared to primary samples from selected normal hematopoietic cells and tissues (Fig 2C). Finally, immunostaining of ETV3 showed nuclear localization in SUP-HD1 and absent protein expression in L-428 (Fig 2E), supporting its reported function as TF, and attesting SUP-HD1 as suitable model to study the role of ETV3 in HL.

Fig 2. ETV3 expression in HL patients and cell lines.

Fig 2

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(A) Expression profiling data (dataset GSE12543, above) for ETV3 in patients with HL, T-cell rich B-cell lymphoma (TCRBL), follicular lym-phoma (FL), Burkitt lymphoma (BL), diffuse large B-cell lymphoma (DLBCL), and normal B-cells: Naïve B-cells (N), memory B-cells (M), germinal center B-cells (GC), plasma cells (P), and dataset GSE39134 with classical HL patients (below). HL patients showing ETV3 overexpression are indicated by arrow heads. (B) ETV3 expression in leukemia/lymphoma cell lines using RNA-seq dataset LL-100. ALCL and HL cell lines are indicated. (C) RQ-PCR expression analysis of ETV3 in HL cell lines (left) and primary cells (right): Hematopoietic stem cell (HSC), peripheral blood cells (PBC), bone marrow (BM), lymph node (LN). (D) Western blot analysis of ETV3 and TUBA in HL cell lines. (E) Immuno-staining of ETV3 (green) in HL cell lines SUP-HD1 (above) and L-428 (below). DAPI (blue) served as nuclear counterstain.

Chromosomal rearrangements and copy number alterations may underlie gene de-regulation in tumor cells and frequently occur in HL [29–31]. The ETV3 locus is located on chromosome 1, band q23. However, inspection of reported karyotypes discounted rearrangements at 1q23 in HDLM-2 and SUP-HD1 [30,31]. In contrast, SUP-HD1 contained several copy number alterations at chromosome 1, including dup(1)(q23) which targeted ETV3 (Fig 3A).

Fig 3. Mechanisms of aberrant ETV3 activation in HL.

Fig 3

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(A) Copy number data for chromosome 1 from HL cell line SUP-HD1. The ETV3 locus at 1q23 is indicated and targeted by a genomic duplication. (B) Potential TF-binding sites at ETV3 obtained from the UCSC genome browser. Binding sites for GATA and AP1 are indicated. (C) RNA-seq expression data for JUNB (above) and GATA3 (below) obtained from the LL-100 dataset. Cell lines derived from ALCL, HL, and T-ALL are indicated. (D) ChIP-seq data obtained from the ENCODE project, show binding of JUN (above) and GATA3 (below) in the promoter region of ETV3, corresponding to their indicated binding sites. (E) RQ-PCR and Western blot analyses of SUP-HD1 after treatment for siRNA-mediated knockdown of JUNB (left) and GATA3 (right), demonstrating their activating input in ETV3 expression.

Analysis of TF binding sites at ETV3 indicated potential regulatory impacts of AP1 and GATA (Fig 3B). AP1 factors JUN and JUNB, and zinc-finger factor GATA3 are reported oncogenes in HL (and additionally in ALCL), highlighting their potential role in ETV3 deregulation [32–35]. RNA-seq data demonstrated elevated expression of JUNB and GATA3 in HL and ALCL cell lines (Fig 3C). Moreover, public chromatin immuno-precipitation (ChIP)-seq data for JUN and GATA3 showed interaction at the promoter region of ETV3, corresponding to their indicated binding sites (Fig 3D). To test the impact of JUNB and GATA3 on ETV3 regulation, we performed siRNA-mediated knockdown experiments in SUP-HD1. The results demonstrated that both, JUNB and GATA3 activated ETV3 expression in HL cells (Fig 3E). Taken together, genomic duplication of the ETV3 locus and aberrant activities of JUNB and GATA3 contributed to deregulated expression of ETV3 in HL.

Next, we examined the functional impact of ETV3 on gene regulation in HL cells. We used SUP-HD1 as model and performed siRNA-mediated knockdown of ETV3 which was confirmed by Western blot (Fig 4A) and RQ-PCR (Fig 4B). Given the reported role of ETS factors in cell and tissue differentiation, we analyzed regulation of genes encoding B-cell master factors EBF1 and PAX5, B-cell differentiation marker CD19, and T-cell master factor GATA3. Our data indicated absent impacts of ETV3 on EBF1 and PAX5 expression while CD19 was suppressed and GATA3 activated (Fig 4B), demonstrating ETV3 and GATA3 as mutual activators. Consistent with these findings, we detected an ETS-binding site in the upstreamregion of CD19 (S7 Fig), indicating a direct regulatory impact by ETV3.

Fig 4. Target gene analyses of ETV3.

Fig 4

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(A) Western blot analysis of ETV3 and TUBA in SUP-HD1 after siRNA-mediated knockdown of ETV3. RQ-PCR analysis of EBF1, PAX5, CD19, and GATA3 (B), and of CELF2, BMP1, and BMP2 (C) in SUP-HD1 after siRNA-mediated knockdown of ETV3. (D) RNA-seq expression data for CELF2 (above) and MIR155 (below) obtained from the LL-100 dataset. Cell lines derived from HL are indicated. (E) RQ-PCR analysis of CELF2 (left) and MIR155 (right) in HL cell lines. (F) RQ-PCR analysis of ETV3 in SUP-HD1 after treatment with BMP4 (left) and BMP-inhibitor dorsomorphin (right).

Gene expression profiling analysis of SUP-HD1 cells treated for ETV3-knockdown revealed additional target gene candidates including suppression of miR155-repressor CELF2 (S1 Table). MIR155 is reportedly an oncogene overexpressed in HL [36]. Furthermore, we detected an ETS-binding site within the intronic region of CELF2 (S7 Fig), supporting the relevance of this potential regulation. Gene set annotation analysis of the top-1000 differentially expressed genes using the online tool DAVID indicated that ETV3 activated developmental processes (chondrocytes, kidney and bone), and suppressed BMP-signalling via BMP1 and BMP2 (S8 Fig, S1 Table).

In addition, RQ-PCR analysis of CELF2, BMP1 and BMP2 after treatment for siRNA-mediated knockdown in SUP-HD1 confirmed that ETV3 suppressed these genes (Fig 4C). In accordance with our results, gene expression data from the LL-100 dataset showed that HL cell lines expressed reduced levels of CELF2 along with elevated MIR155 (Fig 4D) which was confirmed by RQ-PCR analysis (Fig 4E). Moreover, HDLM-2 contained a genomic deletion nearby the locus of MIR155 at 21q21 which may contribute to its activation in this cell line (S9 Fig). Finally, treatment of SUP-HD1 with BMP4 and BMP-pathway inhibitor dorsomorphin demonstrated that BMP-signalling inhibited ETV3 expression (Fig 4F), indicating feedback regulation. Thus, our target gene analysis identified roles for ETV3 in (deregulated) differentiation processes, activation of miR155, and suppression of BMP-signalling.

Our results showing that ETV3 inhibits BMP-signalling prompted us to address in more detail the role of this pathway in HL. An expression heatmap covering selected genes encoding components of the BMP-pathway in HL cell lines is shown in Fig 5A. The observed heterogeneity in expression levels indicated that in each cell line a different strategy to inhibit this pathway had been adopted. For example, KM-H2 expressed high levels of BMP-repressor FSTL3 and low levels of downstream effector SMAD2. These gene activities were confirmed by RQ-PCR analysis (Fig 5B), and correlated with copy number alterations at their loci in this cell line (Fig 5C).

Fig 5. BMP-pathway analysis in HL.

Fig 5

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(A) Heat map showing gene expression data for five HL cell lines and selected components of the BMP-pathway obtained from the LL-100 RNA-seq dataset. Arrows indicate genes FSTL3 and SMAD2. (B) RQ-PCR expression analysis of FSTL3 and SMAD2 in HL cell lines. (C) Copy number data for chromosome 19 (above) and chromosome 18 (below) from cell line SUP-HD1. Gene locis FSTL3 at 19p13 and SMAD2 at 18q21 are indicated. (D) Live-cell imaging results for cell line SUP-HD1 following siRNA-mediated knockdown of ETV3 (left and middle) and with BMP2 (right). The indicated p-values refer to the last time point of treatment and control.

Functional analyses of SUP-HD1 cells treated for ETV3-knockdown by live-cell-imaging indicated that ETV3 supports cell survival but without impacting cell proliferation (Fig 5D). Treatment of SUP-HD1 with BMP2 induced apoptosis (Fig 5D), demonstrating that inhibition of this pathway mediates cell survival. Collectively, our data revealed several modes of BMP-pathway inhibition in HL, including overexpression of FSTL3 and downregulation of SMAD2 via genomic alterations, and inhibition of BMPs via overexpressing ETV3. Suppression of BMP-signalling may, thus, represent an additional oncogenic cell survival mechanism in HL.

ETS1 and FLI1 in Hodgkin lymphoma

Screening of ETS gene activities in HL patients and cell lines revealed several de-regulated members including aberrant downregulation of ETS1 and FLI1 (Fig 6A and 6B), though NLPHL cell line DEV rather showed overexpression of ETS1. RQ-PCR analysis of these genes in HL cell lines demonstrated lowest expression levels in SUP-HD1 while DEV and L-540 expressed elevated ETS1 and FLI1 transcripts (Fig 6C). ETS1 and FLI1 are genomic neighbors located at chromosome band 11q24. Our copy number data showed a genomic loss in SUP-HD1, del(11)(q22.1q25), covering both ETS genes. Furthermore, KM-H2 contained a more extended deletion at that region, del(11)(:q13.2→qter), while DEV showed normal copy numbers for the entire chromosome 11 (Fig 6D). Thus, in classical HL cell lines SUP-HD1 and KM-H2 copy number alterations correspond to suppression of ETS genes ETS1 and FLI1.

Fig 6. Reduced expression of ETS1 and FLI1 in HL.

Fig 6

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(A) Expression profiling data (dataset GSE12543) for ETS1 and FLI1 in patients with HL, T-cell rich B-cell lymphoma (TCRBL), follicular lymphoma (FL), Burkitt lymphoma (BL), diffuse large B-cell lymphoma (DLBCL), normal B-cells: Naïve B-cells (N), memory B-cells (M), germinal center B-cells (GC), plasma cells (P). (B) Expression of ETS1 and FLI1 in leukemia/lymphoma cell lines using RNA-seq dataset LL-100. Cell lines derived from HL and DLBCL are indicated. (C) RQ-PCR analysis of ETS1 (left) and FLI1 (right) in HL cell lines. (D). Copy number data for chromosome 11 from cell lines DEV, KM-H2, and SUP-HD1. The neighboring gene locis ETS1 and FLI1 at 11q24 are indicated.

Recently, Ghosh and colleagues identified RIOK2 as master regulator of hemato-poiesis and showed that RIOK2 activates JAK2 and inhibits ETS1 and FLI1 [37]. Therefore, we addressed the role of RIOK2 in HL. RNA-seq data showed the highest RIOK2 expression in SUP-HD1 (Fig 7A), which was confirmed by RQ-PCR and Western blot analysis (Fig 7B). Of note, HDLM-2 showed elevated RIOK2 protein levels possibly indicating post-transcriptional regulation as mentioned before for ETV3. SiRNA-mediated knockdown experiments in SUP-HD1 demonstrated that RIOK2 activated JAK2 and inhibited ETS1 while sparing FLI1 (Fig 7C). Our copy number data gave no explanation for elevated RIOK2 expression in SUP-HD1 (Fig 7D), while JAK2 was amplified in both, HDLM-2 and SUP-HD1 (S9 Fig). However, TF binding-site analysis revealed a PBX1-site at RIOK2 (Fig 7E). PBX1 is reportedly aberrantly overexpressed in HL patients and cell line SUP-HD1 as shown in Fig 7F [6]. Knockdown of PBX1 resulted in reduced expression of RIOK2 (Fig 7G), demonstrating that PBX1 activated this gene in SUP-HD1. Thus, aberrantly elevated expression of RIOK2 is driven by overexpressed PBX1, mediating suppression of ETS1 in HL. Taken together, our data showed aberrant downregulation of ETS1 (and FLI1) in HL implemented by genomic deletion and RIOK2 overexpression.

Fig 7. RIOK2 inhibits ETS1 in HL.

Fig 7

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(A) Expression of RIOK2 in leukemia/lymphoma cell lines using RNA-seq dataset LL-100. Cell lines derived from HL are indicated. (B) RQ-PCR analysis of RIOK2 in HL cell lines (left). Western blot analysis of RIOK2 and TUBA in HL cell lines and SUP-HD1 treated for RIOK2-knockdown (right). (C) RQ-PCR analysis of SUP-HD1 after treatment for siRNA-mediated knockdown of RIOK2, demonstrating an activating input in JAK2 and an inhibitory input in ETS1 expression while sparing FLI1. (D) Copy number data for chromosome 5 from cell line SUP-HD1. The RIOK2 locus at 5q15 is indicated. (E) Potential TF-binding sites at RIOK2, obtained from the UCSC genome browser. An intronic binding site for PBX1 is indicated. (F) RQ-PCR analysis of PBX1 expression in HL cell lines. (G) RQ-PCR analysis (left) and Western blot analysis (right) of RIOK2 in SUP-HD1 treated for siRNA-mediated knockdown of PBX1.

Discussion

We have previously established the concept of TF-codes which detail normal activi-ties of selected groups of TFs in the hematopoietic compartment and enable identification of deregulated TF-encoding genes in lymphoid and myeloid malignancies [5–8]. In this study, we have established the lymphoid ETS-code which describes physiological expression of 19 ETS genes in early hematopoiesis and lymphopoiesis. ETS genes encode developmental TFs showing specific expression patterns and may, thus, control differentiation processes in particular hematopoietic entities. Our data confirmed previously reported investigations of ETS factors, concerning for example ELF1, ETS1, FLI1, SPI1, and SPIB [17,38]. Of note, the ETS-code covers qualitative data and did not consider quantitative differences between genes and the entities characterized. Here, we analyzed regulation and function of the deregulated ETS genes ETV3, ETS1 and FLI1 in HL as summarized in Fig 8.

Fig 8. Summary of the results generated in this study combined with data from the literature.

Fig 8

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Deregulation of ETS factors ETV3, ETS1 and FLI1 are connected with inhibition of BMP-signalling and B-cell differentiation in HL and highlighted by a blue box. Overexpressed genes are indicated in red, downregulated in green.

We showed that ETV3 represents a novel aberrantly expressed oncogene in HL. Normally, ETV3 was downregulated during B-cell development while genomic duplication and oncogenic activities of the TFs JUNB and GATA3 mediated aberrant ETV3 activation in HL cell line SUP-HD1. These factors may also underlie ETV3 activation in ALCL. Recently, amplification of ETV3 has been reported in breast cancer, supporting copy number gain as potent activating mechanism of this oncogene [39]. Detection of fusion gene ETV3::NCOA2 in independent cell histiocytosis (ICH) underlines a general oncogenic role for ETV3 in hematopoietic malignancies [40,41]. In the myeloid compartment, physiologically expressed ETV3 regulates macrophage differentiation [42,43], highlighting its functional role in developmental processes of myelopoiesis which may be aberrantly reactivated in HL subsets. Consistent with this picture, disturbed B-cell development via deregulated TFs is a recurrent oncogenic mechanism in the pathology of HL [23].

The BMP-pathway plays fundamental roles in controlling development, including normal hematopoiesis and B-cell differentiation, and in leukemia/lymphoma if deregulated [44–47]. Recently, we have shown that aberrantly expressed CHRDL1 promoted inhibition of BMP-signalling resulting in activation of NKL homeobox oncogene MSX1 in T-ALL [48]. Here, we demonstrated that downregulated BMP-signalling activated ETS gene ETV3 in HL. Thus, suppressed BMP-signalling frequently activates developmental TF-encoding genes in lymphoid malignancies. Our data indicated inhibition of BMP activity by ETV3 at the transcriptional level and by FSTL3 at the protein level. Furthermore, the FSTL3 locus was amplified in KM-H2 while that of SMAD2 showed a small genomic deletion nearby, targeting its regulatory region. Downregulation of the BMP-effector SMAD2 in HL also occurs via Epstein-Barr-virus (EBV)-infection [49]. Moreover, MIR155 is overexpressed in HL and targets SMAD2 as well [36,50], supporting the role of SMAD2 as a tumor suppressor in this malignancy. We have shown that CELF2 was downregulated by ETV3. CELF2 is a reported inhibitor of miR155 [51]. Thus, ETV3 promoted inhibition of BMP-signalling via suppression of both, BMPs and SMAD2 (Fig 8).

In addition to upregulation of ETS gene ETV3, we detected and analyzed downreg-ulation of ETS1 (and FLI1) in HL via genomic deletion which confirmed and detailed data from a recent study [52]. Furthermore, ETS1 was downregulated in SUP-HD1 by overexpressed RIOK2. RIOK2 encodes a kinase which represents a novel player in hematopoiesis and regulates several differentiation-related genes, including ETS1, FLI1 and JAK2 [37]. We showed, that the transcription of RIOK2 was activated by PBX1 which, in turn, is aberrantly overexpressed in HL patients and in cell line model SUP-HD1 [6]. Furthermore, ETS1 is a target of miR155 [53], thus representing an additional mechanism of its downregulation. In contrast to HL, ETS1 and FLI1 are aberrantly upregulated in subsets of DLBCL [54], pointing to differences in the pathogenesis of these B-cell lymphomas. In this context, ETS1 expression is more prominent in the ABC subtype while FLI1 dominates in GC DLBCL [55]. ETS1 interacts with and inhibits PRDM1 which plays an important role in plasma cell development [56]. Accordingly, downregulation of ETS1 and FLI1 is required for the final differentiation of this type of B-cells [57–59]. This relationship may be of relevance for HL because the tumor cells share some features with plasma cells [60,61]. Finally, the ETS factors ETS1, FLI1 and GAPBA interact with PAX5, demonstrating an additional regulatory mode of these factors to control B-cell differentiation processes [62]. Taken together, our data highlight that ETS factors are important players in B-cell differentiation and show that their deregulation contributes to the development of lymphoma. Understanding normal and abnormal regulatory networks controlled by ETS factors may serve to improve diagnostics and inform the design and evaluation of novel therapeutic approaches.

Materials and methods

Bioinformatic analyses

Expression data for normal hematopoietic cell types were obtained from Gene Ex-pression Omnibus (GEO, www.ncbi.nlm.nih.gov), using expression profiling dataset GSE56315 [63], and RNA-seq datasets GSE69239, GSE107011, GSE112591 and GSE90834 [64–67]. Gene expression profiling data from HL patients were examined using datasets GSE12453 and GSE39134 [68,69]. Applied cut offs were used as described in previous studies [5–8]. For screening of cell lines we exploited RNA-sequencing data from 100 leukemia/lymphoma cell lines (termed LL-100), available at ArrayExpress (www.ebi.ac.uk/arrayexpress) via E-MTAB-7721 [27]. LL-100 expression data were visualized using the online tool DSMZCellDive [70].

Gene set annotation analysis was performed using the online tool DAVID (provided by the Laboratory of Human Retrovirology and Immunoinformatics, www.david.abcc.ncifcrf.gov) [71]. For screening of TF binding sites we used the UCSC genome browser (www.genome.cse.ucsc.edu). ChIP-seq data were obtained from the ENCODE project and analyzed using the Integrative Genomics Viewer (provided by the Broad Institute, www.broadinstitute.org/data-software-and-tools). Expression profiling data from siRNA-treated SUP-HD1 cells were generated at the Genome Analytics Facility (Helmholtz Centre for Infection Research, Braunschweig, Germany) using HG U133 Plus 2.0 gene chips (Affymetrix) and are available at BioStudies (www.ebi.ac.uk/biostudies) via S-BSST1027. After RMA-background correction and quantile normalization of the spot intensities, profiling data were expressed as ratios of sample means and subsequently log2 transformed. Data processing was performed via R/Bioconductor using public limma and affy packages.

Cell lines and treatments

Cell lines are held by the DSMZ (Braunschweig, Germany) and cultivated as de-scribed previously [72]. All cell lines had been authenticated and tested negative for mycoplasma infection. Modification of gene expression levels was performed using gene specific siRNA oligonucleotides with reference to AllStars negative Control siRNA (siCTR) obtained from Qiagen (Hilden, Germany). SiRNAs (80 pmol) were transfected into 1x106 cells by electroporation using the EPI-2500 impulse generator (Fischer, Heidelberg, Germany) at 350 V for 10 ms. Electroporated cells were harvested after 20 h cultivation. Cell lines were treated with 20 ng/ml bone morphogenetic protein (BMP)2, BMP4 (R&D Systems, Wiesbaden, Germany), 10 μM dorsomorphin (Calbiochem, Darmstadt, Germany) dissolved in DMSO, and harvested after 20 h cultivation.

Proliferation and apoptosis were analyzed by live-cell-imaging using the IncuCyte S3 Live-Cell Analysis System and the associated software package Cell-by-Cell (Essen Bioscience, Essen, Germany). For detection of apoptotic cells, we used the IncuCyte Caspase-3/7 Green Apoptosis Assay diluted at 1:2,000 (Essen Bioscience). These experiments were performed twice with fourfold parallel tests. Statistical analyses were performed for the last time point of the experiments using Student´s T-Test. Standard deviations were indicated as error bars and calculated by the software.

Polymerase chain-reaction (PCR) analyses

Total RNA was extracted from cultivated cell lines using TRIzol reagent (Invitrogen, Darmstadt, Germany). Primary human total RNA was purchased from Biochain/BioCat (Heidelberg, Germany). cDNA was synthesized using 5 μg RNA, random priming and Superscript II (Invitrogen). Real time quantitative (RQ)-PCR analysis was performed using the 7500 Real-time System and commercial buffer and primer sets (Applied Bio-systems/Life Technologies, Darmstadt, Germany). For normalization of expression levels, we quantified the transcripts of TATA box binding protein (TBP). Quantitative analyses were performed as biological replicates and measured in triplicate. Standard deviations are presented in the figures as error bars. Statistical significance was assessed by Student´s T-Test (two-tailed) and the calculated p-values indicated by asterisks (* p<0.05, ** p<0.01, *** p<0.001, n.s. not significant).

Protein analysis

Western blots were generated by the semi-dry method. Protein lysates from cell lines were prepared using SIGMAFast protease inhibitor cocktail (Sigma, Taufkirchen, Germany). Proteins were transferred onto nitrocellulose membranes (Bio-Rad, Munich, Germany) and blocked with 5% dry milk powder dissolved in phosphate-buffered-saline buffer (PBS). The following antibodies were used: alpha-Tubulin (Sigma, #T6199), ETV3 (LSBioSciences, Eching, Germany, #LS-C342337), RIOK2 (MyBioSource, San Diego, CA, USA, #MBS9418079). For loading control, blots were reversibly stained with Poinceau (Sigma) and detection of alpha-Tubulin (TUBA) performed thereafter. Secondary antibodies were linked to peroxidase for detection by West-ern-Lightning-ECL (Perkin Elmer, Waltham, MA, USA). Documentation was performed using the digital system ChemoStar Imager (INTAS, Göttingen, Germany).

Immuno-cytology was performed as follows: cells were spun onto slides and sub-sequently air-dried and fixed with methanol/acetic acid for 90 s. Antibodies were diluted 1:20 in PBS containing 5% BSA and incubated for 30 min. Washing was performed 3 times with PBS. Preparations were incubated with secondary antibody (diluted 1:100) for 20 min. After final washing cells were mounted for nuclear in Vectashield (Vector Laboratories, Burlingame, CA), containing DAPI. Documentation of subcellular protein localization was performed by an Axion A1 microscope using the Axiocam 208 color and software ZEN 3.3 blue edition (Zeiss, Göttingen, Ger-many).

Genomic profiling analysis

For genomic profiling genomic cell line DNA was prepared by the Qiagen Gentra Puregene Kit (Qiagen). Labelling, hybridization and scanning of Cytoscan HD arrays was performed by the Genome Analytics Facility located at the Helmholtz Centre for Infection Research, according to the manufacturer´s protocols (Affymetrix, High Wycombe, UK). Data were interpreted using the Chromosome Analysis Suite software version 3.1.0.15 (Affymetrix) and copy number alterations determined accordingly.

Conclusions

We have described physiological expression patterns of 19 ETS genes in lympho-poiesis, which we termed the lymphoid ETS-code. Exploiting that code for evaluation of ETS genes in HL patients revealed 12 deregulated members in this B-cell malignancy. Detailed analyses of ETV3, ETS1 and FLI1 in HL cell lines discovered deregulatory mechanisms and target genes. Thus, our study should contribute to the understanding of normal and abnormal gene regulatory networks in developing blood and immune cells, findings which may assist diagnostic and therapeutic advances.

Supporting information

S1 Fig. ETS gene activities in early hematopoiesis and T-cell development using RNA-seq dataset GSE69239.

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S2 Fig. ETS gene activities in B-cell development using gene expression profiling dataset GSE56315.

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S3 Fig. ETS gene activities in ILCs using RNA-seq dataset GSE112591, and in ILCP using dataset GSE90834.

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S4 Fig. ETS gene activities in diverse hematopoietic entities using RNA-seq dataset GSE107011.

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S5 Fig. ETS gene activities in lymphoma patients and developing B-cells using gene expression profiling dataset GSE12453.

Abbreviations: Hodgkin lymphoma (HL), T-cell rich B-cell lymphoma (TCRBL), follicular lymphoma (FL), Burkitt lymphoma (BL), diffuse large B-cell lymphoma (DLBCL), and normal B-cells: Naive B-cells (N), memory B-cells (M), germinal center B-cells (GC), plasma cells (P).

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S6 Fig. Selected ETS gene expression in hematopoietic cell lines using RNA-seq dataset LL-100 (E-MTAB-7721).

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S7 Fig. Presentation of potential TF-binding sites at CD19 (above) and CELF2 (below) obtained from the UCSC genome browser.

Binding sites for ETS factor ELK1 are indicated.

(TIF)

Click here for additional data file. (1MB, tif)
S8 Fig. Results calculated by the DAVID online tool for the top-1000 differentially expressed genes in SUP-HD1 treated for siRNA-mediated knockdown of ETV3.

Arrows indicate interesting functions, including differentiation and BMP-signalling.

(TIF)

Click here for additional data file. (1.1MB, tif)
S9 Fig. Copy number analysis of selected HL cell lines for chromosome 21 (above) and 19 (below).

The genes MIR155HG and JAK2 are indicated.

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S1 Table. Gene expression profiling analysis of SUP-HD1 treated for siRNA-mediated knockdown of ETV3.

(XLS)

Click here for additional data file. (7.4MB, xls)
S1 File

(PDF)

Click here for additional data file. (91.8KB, pdf)

Acknowledgments

The authors thank Roderick A.F. MacLeod for critically reading the manuscript.

Data Availability

Generated gene expression profiling data are available from the BioStudies database (S-BSST1027).

Funding Statement

The author(s) received no specific funding for this work.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Fig. ETS gene activities in early hematopoiesis and T-cell development using RNA-seq dataset GSE69239.

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S2 Fig. ETS gene activities in B-cell development using gene expression profiling dataset GSE56315.

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S3 Fig. ETS gene activities in ILCs using RNA-seq dataset GSE112591, and in ILCP using dataset GSE90834.

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S4 Fig. ETS gene activities in diverse hematopoietic entities using RNA-seq dataset GSE107011.

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S5 Fig. ETS gene activities in lymphoma patients and developing B-cells using gene expression profiling dataset GSE12453.

Abbreviations: Hodgkin lymphoma (HL), T-cell rich B-cell lymphoma (TCRBL), follicular lymphoma (FL), Burkitt lymphoma (BL), diffuse large B-cell lymphoma (DLBCL), and normal B-cells: Naive B-cells (N), memory B-cells (M), germinal center B-cells (GC), plasma cells (P).

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S6 Fig. Selected ETS gene expression in hematopoietic cell lines using RNA-seq dataset LL-100 (E-MTAB-7721).

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S7 Fig. Presentation of potential TF-binding sites at CD19 (above) and CELF2 (below) obtained from the UCSC genome browser.

Binding sites for ETS factor ELK1 are indicated.

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S8 Fig. Results calculated by the DAVID online tool for the top-1000 differentially expressed genes in SUP-HD1 treated for siRNA-mediated knockdown of ETV3.

Arrows indicate interesting functions, including differentiation and BMP-signalling.

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S9 Fig. Copy number analysis of selected HL cell lines for chromosome 21 (above) and 19 (below).

The genes MIR155HG and JAK2 are indicated.

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S1 Table. Gene expression profiling analysis of SUP-HD1 treated for siRNA-mediated knockdown of ETV3.

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S1 File

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Data Availability Statement

Generated gene expression profiling data are available from the BioStudies database (S-BSST1027).

Articles from PLOS ONE are provided here courtesy of PLOS

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