Open Access

Differential RelA- and RelB-dependent gene transcription in LTβR-stimulated mouse embryonic fibroblasts

  • Agnes Lovas1,
  • Dörte Radke2, 5, 6,
  • Daniela Albrecht3,
  • Z Buket Yilmaz1, 4,
  • Ulrich Möller2,
  • Andreas JR Habenicht5 and
  • Falk Weih1Email author
BMC Genomics20089:606

DOI: 10.1186/1471-2164-9-606

Received: 03 July 2008

Accepted: 16 December 2008

Published: 16 December 2008

Abstract

Background

Lymphotoxin signaling via the lymphotoxin-β receptor (LTβR) has been implicated in biological processes ranging from development of secondary lymphoid organs, maintenance of spleen architecture, host defense against pathogens, autoimmunity, and lipid homeostasis. The major transcription factor that is activated by LTβR crosslinking is NF-κB. Two signaling pathways have been described, the classical inhibitor of NF-κB α (IκBα)-regulated and the alternative p100-regulated pathway that result in the activation of p50-RelA and p52-RelB NF-κB heterodimers, respectively.

Results

Using microarray analysis, we investigated the transcriptional response downstream of the LTβR in mouse embryonic fibroblasts (MEFs) and its regulation by the RelA and RelB subunits of NF-κB. We describe novel LTβR-responsive genes that were regulated by RelA and/or RelB. The majority of LTβR-regulated genes required the presence of both RelA and RelB, revealing significant crosstalk between the two NF-κB activation pathways. Gene Ontology (GO) analysis confirmed that LTβR-NF-κB target genes are predominantly involved in the regulation of immune responses. However, other biological processes, such as apoptosis/cell death, cell cycle, angiogenesis, and taxis were also regulated by LTβR signaling. Moreover, LTβR activation inhibited expression of a key adipogenic transcription factor, peroxisome proliferator activated receptor-γ (pparg), suggesting that LTβR signaling may interfere with adipogenic differentiation.

Conclusion

Microarray analysis of LTβR-stimulated fibroblasts provided comprehensive insight into the transcriptional response of LTβR signaling and its regulation by the NF-κB family members RelA and RelB.

Background

NF-κB transcription factors are essential for innate and adaptive immunity, cell survival, cellular stress responses, development and maintenance of lymphoid organ structures, and other biological functions [13]. The vertebrate NF-κB family includes five structurally related members, the Rel proteins RelA (p65), RelB, cRel, and the NF-κB proteins p50 and p52. Among the Rel/NF-κB family, only RelA, RelB, and cRel contain C-terminal transcriptional activation domains enabling them to directly regulate transcription. The other two members, p50 and p52, are synthesized as p105 and p100 precursors, respectively. The Rel and NF-κB proteins can form different homo- and heterodimers (for example p50-RelA or p52-RelB) that bind to DNA target sites, so-called κB sites. In resting cells, Rel/NF-κB proteins associate with inhibitory κB molecules (IκBs) and are retained in the cytoplasm as inactive forms [4].

Two major NF-κB signaling pathways can be distinguished, the classical or canonical and the alternative or non-canonical pathway. In response to stimulation of transmembrane receptors like tumor necrosis factor receptor (TNFR)-1 or Toll-like receptor (TLR)-4, signaling cascades are initiated that lead to the liberation of Rel/NF-κB complexes from their IκB molecules. As a result, they translocate to the nucleus and regulate transcription of numerous target genes. This classical pathway involves phosphorylation of IκBα by the NEMO (NF-κB essential modulator)/IKKγ- and IKKβ-containing IκB kinase (IKK) complex followed by its ubiquitin-dependent proteasomal degradation. Regulation of gene transcription is predominantly mediated through p50-RelA and p50-cRel heterodimers and target genes are mainly involved in innate immunity, cell survival, and inflammation. A few inducers of NF-κB, like LTβR, are able to trigger an additional, so-called alternative or non-canonical pathway through the activation of NF-κB-inducing kinase (NIK) and IKKα. The alternative pathway governs gene regulation mainly through p52-RelB heterodimers that are generated from the inactive cytoplasmic p100-RelB complex via signal-dependent processing of the p100 inhibitor to p52. This pathway controls genes that are predominantly involved in adaptive immunity and lymphoid organ development [511]. Recent findings by Hoffmann and colleagues extend this scenario. They could show that not only RelB- but also RelA-containing complexes can be released from the p100 inhibitor after LTβR stimulation [1214].

This report focuses on the transcriptional response downstream of the LTβR and its regulation by RelA and RelB. The role of LTβR signaling in development and organization of secondary lymphoid structures is well documented (reviewed in [8, 1517]). We are interested in similarities and differences in RelA and RelB function in lymphoid organ development. However, a major problem is that RelA-deficient (relA-/-) mice are embryonic lethal due to tumor necrosis factor (TNF)-induced hepatocyte apoptosis [18]. Moreover, RelB-deficient (relB-/-) mice display impaired secondary lymphoid organ development and suffer from an autoinflammatory syndrome that also affects organization and function of lymphoid tissues [19, 20]. Thus, stromal compartments that display LTβR signaling and thereby have an organizational role in the development of lymphoid organs cannot be used for in vivo gene expression studies from the above animals. Therefore, we applied MEFs established from wild-type (wt), relA-/-, and relB-/- mice as an in vitro model system. Also, there is increasing evidence that LTβR functions beyond lymphoid organs, as it is involved in liver regeneration, hepatitis [21], and hepatic lipid metabolism [22]. We therefore hypothesized that LTβR signaling, via RelA and/or RelB, may participate in physiological processes other than lymphorganogenesis. MEFs with different genotypes (wt, relA-/-, and relB-/-) allowed us to dissect specific RelA and RelB activities in the regulation of gene transcription after LTβR stimulation. In wt MEFs, LTβR signals were predominantly transduced by RelA- and/or RelB-containing dimers. Upon LTβR signaling in relA-/- cells, gene regulatory events were mediated by RelB and vice versa in relB-/- cells, changes in gene expression were mediated by RelA. Using this system, we describe novel LTβR-responsive genes that were regulated solely by RelA or RelB or by both RelA and RelB.

Results and discussion

LTβR stimulation of MEFs

For LTβR stimulation, MEFs of each genotype were either left untreated or were treated with agonistic anti-LTβR monoclonal antibody (mAb) for 2.5 or 10 h. For each treatment group, cells from four experiments were pooled. Nuclear protein extracts were used in electrophoretic mobility shift assays (EMSAs) to verify proper LTβR signaling (Figure 1). In wt cells, LTβR signaling resulted in modest induction of κB-binding complexes at the early time point (2.5 h) but strong induction after 10 h of stimulation. Dissection of these complexes with supershifting antibodies revealed that the faster migrating complex contained RelB and the slower migrating complex contained RelA. As expected, in wt cells both RelA and RelB complexes were activated in response to LTβR signaling, whereas in relA-/- cells only RelB- and in relB-/- cells only RelA-containing κB-binding complexes were induced (Figure 1). Recently, slow and relatively weak DNA-binding of NF-κB complexes in response to LTβR ligation was reported [12]. The plateau was reached between 10 and 15 h of LTβR stimulation corresponding to a 2- to 3-fold induction of NF-κB DNA binding. Our results are in agreement with these observations: for each genotype the strongest induction of κB-binding complexes was observed at 10 h. For gene expression profiling we therefore used total RNA isolated from untreated (0 h) and 10 h agonistic anti-LTβR mAb treated wt, relA-/-, and relB-/- MEFs, assuming that stronger DNA-binding activity reflects stronger gene expression changes controlled by NF-κB transcription factor complexes.
https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-9-606/MediaObjects/12864_2008_Article_1799_Fig1_HTML.jpg
Figure 1

Confirmation of LTβR stimulation: induction of RelA- and RelB-containing DNA-binding complexes. Wild-type, relA-/-, and relB-/- MEFs were treated with agonistic anti-LTβR mAb for the indicated times and subsequently nuclear extracts were prepared and analyzed by EMSA for NF-κB DNA-binding activity using an Igκ oligo. Specific Igκ DNA-binding complexes are indicated by arrow (RelA-containing dimers) and arrowhead (RelB-containing dimers). Non-specific DNA binding complexes (ns, lower lane) serve as loading control. Supershift analysis was performed using pre-immune serum (pre-imm. serum), anti-RelA antibody (α-RelA Ab), and anti-RelB antibody (α-RelB Ab). Supershifted complexes are indicated by asterisk.

Global gene expression in response to LTβR stimulation in MEFs

To identify RelA- and RelB-regulated genes after LTβR stimulation, we carried out microarray analysis using total RNA from the experiment described above hybridized to CodeLink UniSet Mouse 20K I bioarrays. For statistical analysis, different genotypes were analyzed separately and significantly differentially expressed genes between time points 0 h and 10 h were identified (p < 0.05). The fold change (FC) threshold was determined from the minimal detectable fold change (MDFC) calculated by the CodeLink Expression Analysis v4.1 software (wt: 1.48; relA-/-: 1.54; relB-/-: 1.36). In response to LTβR stimulation, a total of 528 genes were regulated in wt cells. In line with the moderate NF-κB activation seen in the EMSAs the observed gene regulation was also modest: gene expression changes were in the range of +5-fold (induction) and -5-fold (repression). We assigned the 528 LTβR-responsive genes to 4 categories: genes that were significantly regulated (i) only in wt cells (category I, n = 366), (ii) in wt and relA-/- cells (category II, n = 30), (iii) in wt and relB-/- cells (category III, n = 102), and (iv) genes that were significantly regulated in all 3 genotypes (category IV, n = 30) (Figure 2A; for the list of LTβR-responsive genes in wt cells see Additional file 1).
https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-9-606/MediaObjects/12864_2008_Article_1799_Fig2_HTML.jpg
Figure 2

LTβR-responsive genes can be allocated into distinct categories. (A) Venn-diagram of significantly (p < 0.05) regulated genes. (B) Schematic depiction of gene expression patterns. The four main categories in (A) can be segregated into further subcategories, depending on whether their genes were upregulated or downregulated. The arrows in the plots show the direction of gene expression changes from non-induced (0 h) to the 10 h induced state in response to LTβR stimulation. The first arrow describes gene expression behavior in wild-type, the second in relA-/-, and the third in relB-/- cells. Horizontal arrows indicate lack of change or statistically insignificant change in gene expression. Arrows pointing upwards or downwards indicate significant positive or negative regulation, respectively.

The genes in these four categories could be segregated into further subcategories, which helped us to assign regulatory mechanisms underlying the expression patterns of individual genes (see schematic depiction of gene expression behavior in Figure 2B and lists of genes belonging to different subcategories in Additional files 2, 3, 4, 5).

Category (cat) I genes were significantly regulated only in wt cells in response to LTβR stimulation. This group of genes required both RelA and RelB for their LTβR-dependent activation (cat I/1, n = 161) or repression (cat I/2, n = 205). Therefore, expression of these genes did not significantly change in either of the mutant cell lines in response to agonistic anti-LTβR mAb treatment (Figure 2B, Additional file 2).

Category II genes were significantly regulated in wt and relA-/- cells upon LTβR ligation. Genes upregulated (cat II/1, n = 13) or downregulated (cat II/2, n = 17) in both wt and relA-/- cells, but not significantly regulated in relB-/- cells, were considered to be RelB target genes in response to LTβR signaling. Other theoretical patterns could also be appointed to category II, but we did not find any example in our analysis for these subcategories (cat II/3, n = 0 and cat II/4, n = 0) (Figure 2B, Additional file 3).

Genes belonging to category III were significantly regulated in wt and relB-/- cells in response to LTβR stimulation. Genes upregulated (cat III/1, n = 54) or downregulated (cat III/2, n = 43) in both wt and relB-/- cells, but not significantly regulated in relA-/- cells, were considered to be RelA target genes in response to LTβR signaling. Negligible numbers of genes in category III could also be allocated to cat III/3 and III/4 (n = 3 and n = 2, respectively) (Figure 2B, Additional file 4). However, these genes were not further analyzed. The significantly larger number of RelA- (cat III) compared to RelB-regulated genes (cat II; Figure 2A) is likely to be a consequence of the stronger LTβR-induced DNA binding of RelA compared to RelB complexes (Figure 1).

Category IV genes were significantly regulated in each of the genotypes in response to LTβR ligation. Although eight theoretically possible gene expression behaviors exist, we only found genes that belonged to two easily explainable scenarios: genes were either upregulated (cat IV/1, n = 20), or downregulated (cat IV/2, n = 10) in each genotype upon LTβR signaling (Figure 2B, Additional file 5). Most likely, both RelA and RelB contributed redundantly to their regulation or alternatively, a third factor/pathway controlled these genes in response to LTβR stimulation. JNK (c-Jun N-terminal kinase) is a possible candidate for such a third pathway, as there are indications that LTβR stimulation leads to activation of JNK. However, the experimental setup in those studies was different from ours as LTβR-overexpressing HEK293 cells [23] or treatment of MEFs with the LTβR agonist LIGHT (lymphotoxin-related inducible ligand that competes for glycoprotein D binding to herpesvirus entry mediator on T cells) [24] were studied.

FC values observed in the three cell lines at 10 h compared to 0 h are displayed in a heatmap that also reflects the four categories and their subcategories (Figure 3, for a zoomable/enlarged version of FC heatmaps supplied with gene symbols and GenBank Accession Numbers see Additional file 6).
https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-9-606/MediaObjects/12864_2008_Article_1799_Fig3_HTML.jpg
Figure 3

Fold change heatmaps. Heatmaps displaying the fold change values observed in the three different cell lines at 10 h compared to 0 h. The color code indicates the fold change values between -2.5-fold downregulation (green) and +2.5-fold upregulation (red). Fold change of -2.5 and below are depicted in the brightest green and fold change of +2.5 and above are shown in the brightest red. Black indicates no change in gene expression. Each horizontal line on the heatmap corresponds to one gene. Genes are arranged by their subcategory (see bars on the left) and main categories are divided by a horizontal white line.

Interestingly, in the two subcategories with the largest number of genes both RelA and RelB together were required for LTβR-induced gene regulation (161 cat I/1 genes for their activation and 205 cat I/2 genes for their repression). In case one of the transcription factors was missing the other one was not able to ensure regulation alone, suggesting significant crosstalk between the two NF-κB activation pathways. In response to LTβR stimulation, sequential engagement of the classical and alternative pathway was suggested, resulting in initial DNA binding by RelA followed by RelB complexes [7, 9]. These findings may suggest a scenario where RelA binds first to the DNA in the promoter of category I genes, loosens up chromatin, thereby enabling subsequent DNA binding and gene regulatory action by RelB [25]. Alternatively, since relB is an NF-κB target gene [26] RelA may ensure sufficiently high expression of RelB and in the absence of RelA the reduced RelB levels cannot mediate proper regulation of certain LTβR target genes. This possibility is supported by the observation that in the absence of RelA both RelB protein levels and binding of RelB to κB sites were reduced (Figure 1 and data not shown) [13].

Meta analysis of LTβR-dependent transcriptomes

LTβR signaling is best known in the context of secondary lymphoid organ development and a recent expression profiling study described LTβR-dependent transcriptomes in lymph nodes and follicular dendritic cells (FDCs) [27]. However, increasing evidence suggests that LTβR also plays a role in non-lymphoid organs such as epithelial tissues during embryonic development [28] and adult liver [21, 22].

To interpret our results in the light of other studies investigating LTβR signaling, we compared our LTβR-responsive genes with two recently published LTβR-dependent transcriptomes. Huber et al. identified transcripts in murine mesenteric lymph nodes affected in vivo by the administration of a soluble LTβR-Ig decoy receptor which blocks LTβR signaling [27]. A gene cluster of 80 unique transcripts that showed decreased expression after LTβR blockade was further analyzed. Twelve genes in this cluster were also associated with germinal centers (GCs)/FDC. A few common genes were found between our analysis and the LTβR-dependent transcriptomes described by Huber et al. Dclk1 and enpp2 (doublecortin-like kinase 1; GenBank Accession Number: NM_019978 and ectonucleotide pyrophosphatase/phosphodiesterase 2 or autotaxin; GenBank Accession Number: NM_015744) expression was moderately decreased 3 d after LTβR blockade (FC: 0.70× and 0.66×, respectively) [27]. In our hands, both genes were upregulated in response to LTβR stimulation in a RelA-dependent manner (cat III/1, for enpp2 see also Table 12). Enpp2 was also found to be associated with GC/FDC in mesenteric lymph nodes [27]. Moreover, Enpp2 (also called autotaxin) has been recently described as a new molecule in lymphocyte homing through high endothelial venules (HEVs) [29]. Collectively, these findings suggest that LTβR, in addition to its well-described effect on the HEV differentiation program [30], further contributes via RelA-dependent upregulation of enpp2 to lymphocyte homing through HEVs. Unfortunately, we could not detect further genes with a similar regulation pattern in our and Huber and colleagues' studies. This lack of overlap could be the consequence of several reasons: (i) different modes of function and kinetics of antagonistic LTβR-Ig vs agonistic anti-LTβR mAb application, (ii) incubation time (3 d treatment with LTβR-Ig vs 10 h treatment with agonistic anti-LTβR mAb), or (iii) in vivo collection of different cell types influenced by the treatment vs in vitro cell culture system using MEFs.

Lo et al. described a hepatic gene expression profile of wt vs lck-LIGHT transgenic mice (overexpressing the LTβR ligand LIGHT on the surface of T lymphocytes) [22]. A group of significantly regulated genes (n = 19) involved in lipid and cholesterol metabolism was identified. The gene that displayed the highest level of regulation (23-fold repression in transgenic vs wt mice) encodes for hepatic lipase, a key enzyme in lipid metabolism. We did not observe repression of hepatic lipase in our experiments, most probably due to its restricted expression on the surface of hepatocytes. However, we found another gene belonging to the lipid/cholesterol metabolism-related group described by Lo and colleagues. Ralgds (ral guanine nucleotide dissociation stimulator, GenBank Accession Number: NM_009058) expression was increased in the liver of transgenic mice and also upregulated in our LTβR stimulation experiments, belonging to the RelA-responsive genes (cat III/1, Table 12).

Gene Ontology (GO) enrichment analysis

Our goal was not only to define the LTβR-dependent transcriptome in MEFs, but also to assign regulatory mechanisms to LTβR signaling, i.e. to examine which part of the LTβR transcriptome is regulated by RelA, RelB, or both. We started out with GO enrichment analysis of significantly regulated genes to identify biological processes, molecular functions, and cellular components putatively regulated in the categories described above. Compared to molecular functions and cellular components, GO analysis of biological processes yielded the most conclusive results.

First, GO analysis was performed on the total LTβR transcriptome in wt cells to see how LTβR signaling influences biological processes in these fibroblasts, regardless whether these genes were also regulated in relA-/- or relB-/- cells (Category: Total wild-type, Table 1). For interpretation of our data we chose GO terms with p < 0.01. As lower limit, we did not consider GO terms with less than 3 annotated genes in the list of differentially regulated genes since they are too specific. As upper limit we did not use GO terms represented by more than 600 genes on the microarray since they are too general. Among the considered GO terms we found that apoptosis/cell death (A/CD)- and cell cycle (CCY)-related processes were overrepresented. We also found that genes annotated with "response to biotic stimulus", "immune system process" (immune related (IR) features) as well as "blood vessel morphogenesis" and "angiogenesis" (blood vessel development related (BR) features) were enriched. Collectively, these data indicate that LTβR signaling largely influences cell survival/cell proliferation features. Moreover, it has an impact on immune responses and blood vessel development/angiogenesis related processes. Since these GO terms were found in LTβR-stimulated "non-immune" fibroblasts it is likely that LTβR signaling regulates similar biological processes in stromal cells of secondary lymphoid tissues governing lymphorganogenesis and maintaining lymphoid tissue architecture.
Table 1

Gene Ontology analysis of total LTβR transcriptome in wild-type cells

GO number

GO term

Type of biological process

p value

n sel.

n tot.

GO:0007049

Cell cycle

CCY

1.80E-05

39

559

GO:0006915

Apoptosis

A/CD

1.00E-04

34

499

GO:0008219

Cell death

A/CD

0.00011

35

523

GO:0016265

Death

A/CD

0.00011

35

523

GO:0012501

Programmed cell death

A/CD

0.00012

34

503

GO:0006259

DNA metabolic process

CCY

0.00016

32

469

GO:0022402

Cell cycle process

CCY

0.00034

30

447

GO:0042981

Regulation of apoptosis

A/CD

0.00063

23

319

GO:0043067

Regulation of programmed cell death

A/CD

0.00068

23

321

GO:0009607

Response to biotic stimulus

IR

0.0035

11

124

GO:0006260

DNA replication

CCY

0.0037

10

107

GO:0043066

Negative regulation of apoptosis

A/CD

0.0045

11

128

GO:0000074

Regulation of progression through cell cycle

CCY

0.0047

19

287

GO:0043069

Negative regulation of programmed cell death

A/CD

0.0048

11

129

GO:0051726

Regulation of cell cycle

CCY

0.0051

19

289

GO:0002376

Immune system process

IR

0.0053

30

534

GO:0030968

Unfolded protein response

 

0.0054

3

11

GO:0007610

Behavior

 

0.0054

17

249

GO:0009953

Dorsal/ventral pattern formation

 

0.0057

5

37

GO:0016567

Protein ubiquitination

 

0.0064

5

35

GO:0006730

One-carbon compound metabolic process

 

0.0067

7

65

GO:0048514

Blood vessel morphogenesis

BR

0.0078

12

157

GO:0040029

Regulation of gene expression, epigenetic

 

0.0082

5

37

GO:0007631

Feeding behavior

 

0.0084

4

24

GO:0001525

Angiogenesis

BR

0.0087

10

121

GO:0006171

cAMP biosynthetic process

 

0.0089

3

13

GO:0051094

Positive regulation of developmental process

 

0.0092

6

53

Analysis of functional enrichment was performed employing Fisher's exact test. The number of genes annotated with a specific GO term was determined for the list of differentially expressed genes (n sel.) and compared to all GO annotated genes on the array (n tot.). The resulting p values (p < 0.01) were used to rank GO terms according to their significance. Terms with more than 600 genes on the array or less than 3 genes on the list of investigated genes were regarded as too general or too specific, respectively, and excluded from the analysis. A/CD, apoptosis/cell death; CCY, cell cycle; IR, immune related; BR, blood vessel development related.

Next, we carried out GO analysis for the four main categories and for all subcategories with at least 20 genes. Interpretation of the data was performed applying the same criteria as above. GO analysis of category I genes revealed those biological processes that were overrepresented only in LTβR-stimulated wt cells, i.e. in the presence of both RelA and RelB (Table 2). Amongst these processes, CCY-related terms dominated. Subsequently, we analyzed cat I/1 (containing genes that were upregulated exclusively in wt cells) and found enrichment of IR- and cell/biological adhesion (important events in immune cell migration)-related terms on the list of biological processes (Table 3). This finding indicates that in the absence of RelA or RelB a considerable portion of LTβR-stimulated immune response-related events cannot be carried out; fibroblasts need both molecules to execute these processes. In cat I/2 (containing genes that are downregulated exclusively in wt cells) we found enrichment of CCY-related terms on the list of overrepresented biological processes (Table 4). This finding indicates that in wt cells an important action of RelA and RelB is to downregulate numerous genes that are implicated in cell cycle regulation in response to LTβR signaling.
Table 2

Gene Ontology analysis of category I

GO number

GO term

Type of biological process

p value

n sel.

n tot.

GO:0006259

DNA metabolic process

CCY

1.40E-05

27

469

GO:0007049

Cell cycle

CCY

1.80E-05

30

559

GO:0022402

Cell cycle process

CCY

0.00033

23

447

GO:0040029

Regulation of gene expression, epigenetic

 

0.0016

5

37

GO:0006260

DNA replication

CCY

0.0036

8

107

GO:0022403

Cell cycle phase

CCY

0.0041

12

211

GO:0006730

One-carbon compound metabolic process

 

0.0041

6

65

GO:0051301

Cell division

CCY

0.0045

11

187

GO:0031497

Chromatin assembly

 

0.0047

5

47

GO:0016458

Gene silencing

 

0.0068

3

17

GO:0009953

Dorsal/ventral pattern formation

 

0.0079

4

34

GO:0043543

Protein amino acid acylation

 

0.008

3

18

GO:0000278

Mitotic cell cycle

CCY

0.0081

10

175

GO:0016567

Protein ubiquitination

 

0.0087

4

35

GO analysis was performed the same way as for category "total wild-type" described in Table 1 legend. CCY, cell cycle.

Table 3

Gene Ontology analysis of category I/1

GO number

GO term

Type of biological process

p value

n sel.

n tot.

GO:0045087

Innate immune response

IR

0.0027

4

58

GO:0002526

Acute inflammatory response

IR

0.0037

4

63

GO:0007155

Cell adhesion

IR

0.0054

11

447

GO:0022610

Biological adhesion

IR

0.0054

11

447

GO analysis was performed the same way as for category "total wild-type" described in Table 1 legend. IR, immune related.

Table 4

Gene Ontology analysis of category I/2

GO number

GO term

Type of biological process

p value

n sel.

n tot.

GO:0007049

Cell cycle

CCY

3.10E-07

24

559

GO:0006259

DNA metabolic process

CCY

9.50E-07

21

469

GO:0022402

Cell cycle process

CCY

7.00E-06

19

447

GO:0022403

Cell cycle phase

CCY

2.40E-05

12

211

GO:0051301

Cell division

CCY

3.90E-05

11

187

GO:0000278

Mitotic cell cycle

CCY

0.00011

10

175

GO:0006730

One-carbon compound metabolic process

 

0.00022

6

65

GO:0006468

Protein amino acid phosphorylation

 

0.00025

17

487

GO:0006260

DNA replication

CCY

0.00055

7

107

GO:0000279

M phase

CCY

0.00057

9

176

GO:0016310

Phosphorylation

 

0.00076

17

536

GO:0009953

Dorsal/ventral pattern formation

 

0.001

4

34

GO:0040029

Regulation of gene expression, epigenetic

 

0.0014

4

37

GO:0007067

Mitosis

CCY

0.0015

7

126

GO:0000087

M phase of mitotic cell cycle

CCY

0.0015

7

127

GO:0043543

Protein amino acid acylation

 

0.0016

3

18

GO:0007224

Smoothened signaling pathway

 

0.0038

3

24

GO:0006913

Nucleocytoplasmic transport

 

0.004

5

79

GO:0051169

Nuclear transport

 

0.004

5

79

GO:0007178

Transmembrane receptor protein serine/threonine kinase signaling pathway

 

0.0083

4

60

GO:0022613

Ribonucleoprotein complex biogenesis and assembly

 

0.0093

6

135

GO:0035295

Tube development

 

0.0096

6

136

GO analysis was performed the same way as for category "total wild-type" described in Table 1 legend. CCY, cell cycle.

Since cat II/1 and II/2 had only few genes (n = 13 and n = 17, respectively), investigation of GO terms for these groups of genes was not meaningful. GO analysis of the main category II (containing genes that were regulated – either up or down – in wt and relA-/- cells, n = 30) revealed only one enriched GO term, the cell cycle (Table 5). Thus, in response to LTβR signaling a characteristic feature of RelB was to influence cell cycle-related events.
Table 5

Gene Ontology analysis of category II

GO number

GO term

Type of biological process

p value

n sel.

n tot.

GO:0007049

Cell cycle

CCY

0.0059

5

559

GO analysis was performed the same way as for category "total wild-type" described in Table 1 legend. CCY, cell cycle.

Category III contains genes that were regulated – either up or down – in wt and relB-/- cells in response to LTβR stimulation. Among enriched biological processes, the new and in previous categories not yet observed theme taxis and response to external/chemical stimulus (T) dominated, but A/CD-related events also appeared (Table 6). As expected, the theme IR was also represented among the enriched biological processes. This shows that RelA is not only a signal transducer for immune responses and apoptosis/cell death, but also has an impact on the transcription of taxis- and stimulus-responsive genes following LTβR ligation. Among the enriched biological processes of cat III/1 we observed again overrepresentation of T and IR processes (Table 7), revealing that in response to LTβR signaling RelA strongly influenced these events via upregulation of several genes. In cat III/2 we found genes that were repressed by RelA. In this subcategory RelA on one hand regulated several BR events. On the other hand, it turned out to be a negative regulator of genes involved in ion homeostasis (ION) downstream of the LTβR (Table 8).
Table 6

Gene Ontology analysis of category III

GO number

GO term

Type of biological process

p value

n sel.

n tot.

GO:0006939

Smooth muscle contraction

 

0.00018

3

16

GO:0048675

Axon extension

 

0.00027

3

18

GO:0006935

Chemotaxis

T

0.00058

5

95

GO:0042330

Taxis

T

0.00058

5

95

GO:0009605

Response to external stimulus

T

0.0011

9

364

GO:0006936

Muscle contraction

 

0.0011

4

64

GO:0007610

Behavior

T

0.002

7

249

GO:0048858

Cell projection morphogenesis

 

0.003

6

200

GO:0032990

Cell part morphogenesis

 

0.003

6

200

GO:0030030

Cell projection organization and biogenesis

 

0.003

6

200

GO:0007626

Locomotory behavior

T

0.0072

5

169

GO:0042981

Regulation of apoptosis

A/CD

0.0077

7

319

GO:0043067

Regulation of programmed cell death

A/CD

0.0079

7

321

GO:0042221

Response to chemical stimulus

T

0.0082

7

323

GO:0006915

Apoptosis

A/CD

0.009

9

499

GO:0012501

Programmed cell death

A/CD

0.0094

9

503

GO:0048522

Positive regulation of cellular process

 

0.0096

10

596

GO:0006955

Immune response

IR

0.0097

7

334

GO analysis was performed the same way as for category "total wild-type" described in Table 1 legend. T, taxis, response to external/chemical stimulus; A/CD, apoptosis/cell death; IR, immune related.

Table 7

Gene Ontology analysis of category III/1

GO number

GO term

Type of biological process

p value

n sel.

n tot.

GO:0006955

Immune response

IR

2.00E-04

7

334

GO:0009605

Response to external stimulus

T

0.00034

7

364

GO:0006935

Chemotaxis

T

0.00041

4

95

GO:0042330

Taxis

T

0.00041

4

95

GO:0002376

Immune system process

IR

0.00065

8

534

GO:0007610

Behavior

T

0.0022

5

249

GO:0007626

Locomotory behavior

T

0.0035

4

169

GO:0006954

Inflammatory response

IR

0.0036

4

171

GO:0006952

Defense response

IR

0.0052

5

305

GO:0002252

Immune effector process

IR

0.0064

3

102

GO:0042221

Response to chemical stimulus

T

0.0066

5

323

GO analysis was performed the same way as for category "total wild-type" described in Table 1 legend. IR, immune related; T, taxis, response to external/chemical stimulus.

Table 8

Gene Ontology analysis of category III/2

GO number

GO term

Type of biological process

p value

n sel.

n tot.

GO:0006939

Smooth muscle contraction

 

1.40E-05

3

16

GO:0006936

Muscle contraction

 

3.90E-05

4

64

GO:0001525

Angiogenesis

BR

0.00046

4

121

GO:0048514

Blood vessel morphogenesis

BR

0.0012

4

157

GO:0048646

Anatomical structure formation

BR

0.0012

4

159

GO:0030005

Cellular di-, tri-valent inorganic cation homeostasis

ION

0.0016

3

77

GO:0055066

Di-, tri-valent inorganic cation homeostasis

ION

0.0017

3

78

GO:0008015

Circulation

BR

0.0017

3

79

GO:0030003

Cellular cation homeostasis

ION

0.0021

3

84

GO:0001568

Blood vessel development

BR

0.0021

4

182

GO:0055080

Cation homeostasis

ION

0.0021

3

85

GO:0006873

Cellular ion homeostasis

ION

0.0022

3

86

GO:0055082

Cellular chemical homeostasis

ION

0.0022

3

86

GO:0001944

Vasculature development

BR

0.0023

4

185

GO:0050801

Ion homeostasis

ION

0.003

3

96

GO:0065008

Regulation of biological quality

 

0.004

5

354

GO:0065008

Chemical homeostasis

ION

0.0062

3

124

GO:0007507

Heart development

BR

0.0088

3

141

GO analysis was performed the same way as for category "total wild-type" described in Table 1 legend. BR, blood vessel development related; ION, ion homeostasis.

Category IV contains genes that were regulated – either up or down – in each of the cell types in response to LTβR stimulation (Table 9). IR processes were overrepresented, but the terms related to hematopoietic or lymphoid organ development (LY) and taxis (T) were also present on the list of enriched biological processes. Unfortunately, we could not analyze cat IV/2, as it comprises too few genes (n = 10). Cat IV/1 contains 20 genes that were upregulated, irrespective of the genotype (Table 10). These genes primarily belong to IR and T. Possibly, RelA and RelB redundantly regulate these events or alternatively a RelA- and RelB-independent third factor/pathway (e.g. JNK) controls these biological processes following LTβR ligation. Table 11 shows a summary of our GO analysis.
Table 9

Gene Ontology analysis of category IV

GO number

GO term

Type of biological process

p value

n sel.

n tot.

GO:0002376

Immune system process

IR

4.40E-05

7

534

GO:0006955

Immune response

IR

0.00038

5

334

GO:0045595

Regulation of cell differentiation

 

0.0013

3

113

GO:0006952

Defense response

IR

0.0026

4

305

GO:0042221

Response to chemical stimulus

T

0.0032

4

323

GO:0006954

Inflammatory response

IR

0.0043

3

171

GO:0048534

Hemopoietic or lymphoid organ development

LY

0.0064

3

197

GO:0050793

Regulation of developmental process

 

0.0067

3

201

GO:0002520

Immune system development

IR

0.0078

3

212

GO analysis was performed the same way as for category "total wild-type" described in Table 1 legend. IR, immune related; T, taxis, response to external/chemical stimulus; LY, hematopoietic or lymphoid organ developmental processes.

Table 10

Gene Ontology analysis of category IV/1

GO number

GO term

Type of biological process

p value

n sel.

n tot.

GO:0002376

Immune system process

IR

1.40E-05

6

534

GO:0006955

Immune response

IR

2.4E-05

5

334

GO:0006952

Defense response

IR

0.00032

4

305

GO:0006954

Inflammatory response

IR

0.00091

3

171

GO:0009611

Response to wounding

IR

0.0024

3

240

GO:0015031

Protein transport

 

0.0024

4

523

GO:0045184

Establishment of protein localization

 

0.0029

4

546

GO:0008104

Protein localization

 

0.0037

4

586

GO:0042221

Response to chemical stimulus

T

0.0056

3

323

GO:0006886

Intracellular protein transport

 

0.0057

3

326

GO:0009605

Response to external stimulus

T

0.0078

3

364

GO analysis was performed the same way as for category "total wild-type" described in Table 1 legend. IR, immune related; T, taxis, response to external/chemical stimulus.

Table 11

Summary of Gene Ontology analysis results

Category/Subcategory

Enriched biological processes

Regulatory molecules downstream of LTβR, and their effects on the gene expression

Total wild-type

A/CD

CCY

IR

BR

Molecules not assignable – up and downregulation

Cat I

CCY

RelA and RelB together – up and downregulation

Cat I/1

IR

RelA and RelB together – upregulation

Cat I/2

CCY

RelA and RelB together – downregulation

Cat II

CCY

RelB – up and downregulation

Cat II/1

Not investigated

RelB – upregulation

Cat II/2

Not investigated

RelB – downregulation

Cat III

T

A/CD

IR

RelA – up and downregulation

Cat III/1

T

IR

RelA – upregulation

Cat III/2

ION

BR

RelA – downregulation

Cat IV

IR

T

LY

RelA and RelB via redundant effects – up and downregulation

OR

Third pathway – up and downregulation

Cat IV/1

IR

T

RelA and RelB via redundant effects – upregulation

OR

Third pathway – upregulation

Cat IV/2

Not investigated

RelA and RelB via redundant effects – downregulation

OR

Third pathway – downregulation

Summary of GO analysis: categories/subcategories with their respective enriched biological processes and the assigned regulatory mechanisms are listed. A/CD, apoptosis/cell death; CCY, cell cycle; IR, immune related; BR, blood vessel development related; T, taxis, response to external/chemical stimulus; ION, ion homeostasis; LY, hematopoietic or lymphoid organ developmental processes. Since cat II/1, II/2 and cat IV/2 had only few genes (n = 13, 17 and 10, respectively) they were not investigated for GO terms.

Verification of microarray results by qRT-PCR

The changes in mRNA levels of several known as well as novel LTβR-responsive genes on the microarray were confirmed by quantitative real-time reverse-transcription-PCR (qRT-PCR), using RNA from three independent LTβR stimulation experiments (Table 12). In agreement with previous reports, we also found induction of nfkb2 [5, 6], ccl2/mcp1 [6], and ikba expression [31] in LTβR-stimulated wt fibroblasts. In addition, our data indicate that both RelA and RelB redundantly contributed to the proper regulation of these genes in response to LTβR stimulation. However, we did not observe LTβR-dependent upregulation of lymphorganogenic chemokines as described by others. Ccl21, ccl19, cxcl13, and cxcl12 were shown to be LTβR-induced genes in spleen 8 h after peritoneal injection of an agonistic anti-LTβR mAb [5]. Possibly, cell context-specific signaling accounts for the difference observed between splenocytes and established 3T3 fibroblasts used in our experiments. Basak et al. observed modest upregulation of cxcl13 and ccl21 in established wt 3T3 fibroblasts after 24 h treatment with agonistic anti-LTβR mAb [13]. To reduce indirect gene regulatory effects due to rather long stimulation we activated LTβR signaling only for 10 h, where modulation of these chemokines was not observed.
Table 12

Verification of microarray results by qRT-PCR

Gene Symbol and GenBank Accession Number

CodeLink bioarrays FC and p value (in brackets) for wt/relA-/-/relB-/- cells and corresponding subcategory

qRT-PCR FC ± SD for wt/relA-/-/relB-/- cells and corresponding subcategory

Cx3cl1

NM_009142

1.77 (0.00370)/0.90 (>0.05)/0.96 (>0.05), I/1

1.66 ± 0.22/0.89 ± 0.10/1.08 ± 0.29, I/1

Pparg

NM_011146

0.65 (0.00690)/0.55 (0.01800)/1.32 (>0.05), II/2

0.50 ± 0.02/0.48 ± 0.04/0.81 ± 0.11, II/2

Ralgds *

NM_009058

2.24 (0.00750)/1.48 (>0.05)/1.58 (0.00140), III/1

2.03 ± 0.42/1.13 ± 0.10/1.17 ± 0.16, I/1 – not verified in relB -/- cells

Enpp2 *

NM_015744

2.28 (0.00150)/1.36 (>0.05)/5.10 (0.00070), III/1

1.85 ± 0.30/1.35 ± 0.27/3.29 ± 0.91, III/1

Birc3

NM_007464

2.77 (0.00090)/1.34 (>0.05)/2.94 (0.00140), III/1

2.86 ± 0.73/1.27 ± 0.11/2.99 ± 0.47, III/1

Cxcl10/IP10

NM_021274

1.91 (0.00450)/0.58 (>0.05)/2.14 (0.03000), III/1

2.58 ± 0.21/1.28 ± 0.39/2.67 ± 0.20, III/1

Irf1

NM_008390

1.96 (0.00270)/2.05 (>0.05)/2.90 (0.00075), III/1

2.67 ± 0.32/1.77 ± 0.77/2.15 ± 0.19, III/1

Cd74

NM_010545

3.11 (0.00300)/0.82 (>0.05)/3.46 (0.00070), III/1

5.01 ± 0.99/1.06 ± 0.18/4.77 ± 0.56, III/1

Fosl1

NM_010235

0.49 (0.00290)/0.86 (>0.05)/0.42 (0.00070), III/2

0.46 ± 0.09/0.90 ± 0.09/0.47 ± 0.10, III/2

Nfkb2

NM_019408

2.18 (0.0029)/1.57 (0.0016)/1.81 (0.0007), IV/1

2.04 ± 0.37/2.43 ± 0.50/2.74 ± 0.54, IV/1

Ccl2/MCP1

NM_011333

2.10 (0.00120)/2.84 (0.0011)/2.99 (0.00099), IV/1

2.29 ± 0.42/3.18 ± 0.13/6.31 ± 1.63, IV/1

Nfkbia/IκBα

NM_010907

2.00 (0.00064)/2.19 (0.00270)/3.42 (0.00140), IV/1

1.77 ± 0.16/2.44 ± 0.34/3.92 ± 0.42, IV/1

Ccl7/MCP3

NM_013654

2.22 (0.00041)/1.99 (0.04700)/4.35 (0.00140), IV/1

2.77 ± 0.13/3.15 ± 0.15/5.29 ± 1.68, IV/1

Cxcl1/KC

NM_008176

2.40 (0.00580)/1.77 (0.01600)/1.80 (0.00160), IV/1

2.40 ± 0.46/1.31 ± 0.61/3.41 ± 0.88, III/1 – not verified in relA -/- cells

Id2

NM_010496

0.42 (0.00440)/0.60 (0.04000)/0.39 (0.00075), IV/2

0.47 ± 0.11/0.75 ± 0.05/0.57 ± 0.14, IV/2

qRT-PCR using RNA from 3 independent LTβR stimulation experiments confirmed changes in mRNA levels of several known as well as novel LTβR-responsive genes on the microarray. Gene names (Gene Symbol) and GenBank Accesion Numbers are shown in the first column. FC values with corresponding p values in brackets, observed in the 3 cell lines (wt; relA-/-; relB-/-) at 10 h with CodeLink bioarrays and corresponding subcategories (in bold) are displayed in the second column. FC values with corresponding standard deviations (SD), observed in the 3 cell lines (wt; relA-/-; relB-/-) at 10 h with qRT-PCR using RNA from 3 independent LTβR stimulation experiments and corresponding subcategories (in bold) are displayed in the third column. Genes that are discussed in chapter "Meta analysis of LTβR-dependent transcriptomes" are indicated by an asterisk and genes that are discussed in chapter "Verification of microarray results by qRT-PCR" are listed in bold.

Table 13

LTβR responsive qRT-PCR verified genes in literature

Gene Symbol and GenBank Accession Number

LTβR responsiveness

„reference“ if known/„this study“ if new

In response to LTβR stimulation, transcription is regulated by RelA or RelB, + or - or 0 manner „reference“ if known/„this study“ if new

Cx3cl1

NM_009142

This study

+ regulation by RelA and RelB together, this study

Pparg

NM_011146

This study

0 RelA, this study

- RelB, this study

Ralgds *

NM_009058

Lo et al., 2007 [22]

Mode of regulation uncertain: RelA either alone, or together with RelB enhances Ralgds expression.

Enpp2 *

NM_015744

Huber et al., 2005 [27]

+ RelA, this study

0 RelB, this study

Birc3

NM_007464

This study

+ RelA, this study

0 RelB, this study

Cxcl10/IP10

NM_021274

Lukashev et al., 2006 [34]

+ RelA, this study

0 RelB, this study

Irf1

NM_008390

Kutsch et al., 2008 [41]

+ RelA, this study

0 RelB, this study

Cd74

NM_010545

This study

+ RelA, this study

0 RelB, this study

Fosl1

NM_010235

This study

- RelA, this study

0 RelB, this study

Nfkb2

NM_019408.1

Dejardin et al., 2002 [5]

Derudder et al., 2003 [6]

+ RelA, Dejardin et al., 2002 [5]

+ RelB, this study

Ccl2/MCP1

NM_011333

Derudder et al., 2003 [6]

+ RelA, this study

+ RelB, this study

Nfkbia/IκBα

NM_010907

Bonizzi et al., 2004 [31]

+ RelA, this study

+ RelB, this study

Ccl7/MCP3

NM_013654

This study

+ RelA, this study

+ RelB, this study

Cxcl1/KC

NM_008176

This study

+ RelA, this study

Positive regulation by RelB is uncertain.

Id2

NM_010496

This study

- RelA, this study

- RelB, this study

Genes that are discussed in chapter "Meta analysis of LTβR-dependent transcriptomes" are indicated by an asterisk and genes that are discussed in chapter "Verification of microarray results by qRT-PCR" are listed in bold.

Importantly, we verified novel LTβR-responsive genes and appointed regulatory molecules to them. For a complete list of verified genes see Table 12. Here, some of those verified genes are discussed in more detail.

GO analysis revealed that LTβR stimulation resulted in the regulation of IR processes (Table 11). Except category "Total wild-type", where we could not assign regulatory molecules, in all categories where IR processes were enriched, RelA alone or together with RelB acted as a positive factor. Cx3cl1 (chemokine C-X3-C motif ligand 1/fractalkine) is one of the IR genes in cat I/1. Several studies document that NF-κB upregulates cx3cl1, e.g. in rat aortic endothelial cells upon interleukin-1β (IL-1β), TNF, and lipopolysaccharide treatment [32] or in human coronary artery smooth muscle cells [33]. The latter work shows that atherogenic lipids induce adhesion of artery smooth muscle cells to macrophages via the upregulation of cx3cl1 in a TNF/NF-κB-dependent manner. In our experiments this gene was upregulated in response to LTβR stimulation dependent on RelA and RelB. This data suggests that LTβR, via employing RelA and RelB together, may act as a proatherogenic factor.

IR- and T-related processes were also enriched in cat III and cat III/1 according to the GO analysis. Cd74/ii (invariant polypeptide of major histocompatibility complex, class II antigen-associated) and cxcl10/ip10 (chemokine C-X-C motif ligand 10/interferon-inducible protein-10) are two genes in cat III/1 and assigned to IR and T. CD74/Ii is involved in antigen processing and presentation and CXCL10 is chemotactic for monocytes and T cells. Moreover, expression of CXCL10, along with two other CXCR3-binding chemokines CXCL9 and CXCL11, can be induced in carcinoma cells by LTβR agonists. These chemokines function as potent chemoattractants for activated T, NK, and dendritic cells, which may contribute to antitumor immune responses [34]. In our experiments, expression of cd74/ii and cxcl10/ip10 was upregulated by LTβR signaling in wt and relB-/- cells. Thus, LTβR signaling via RelA may (i) attract T lymphocytes and promote antigen presentation by dendritic cells in the context of MHC class II and (ii) facilitate antitumor responses against cancer cells.

As indicated by GO analysis, IR- and T-related biological processes were significantly regulated in cat IV and cat IV/1. Amongst others, genes encoding proteins that participate in innate immune responses, like ccl7/mcp3, are also represented in these groups. Ccl7/mcp3 encodes the proinflammatory chemokine C-C motif ligand 7/monocyte chemotactic protein-3. Expression of ccl7/mcp3 was upregulated by LTβR signaling in each of the genotypes, indicating redundant positive regulation by RelA and RelB or upregulation via another RelA- and RelB-independent pathway.

Collectively, positive regulation of the expression of proinflammatory chemokines like cx3cl1, cxcl10, ccl7 (but also others, see Table 12) by LTβR suggests that LTβR signaling, besides regulating development and organization of secondary lymphoid structures, also participates in innate/inflammatory immune responses and for that primarily RelA action seems to be necessary.

Moreover, we found that LTβR signaling functions beyond the regulation of immune responses and organization of lymphoid structures. PPARγ (peroxisome proliferator activated receptor γ) is a key-regulatory transcription factor in the process of adipocyte differentiation and activation of PPARγ promotes the storage of fat [35]. The work of Fu and colleagues suggests that LTβR affects lipid homeostasis by downregulating hepatic lipase expression [22]. Hepatic lipase is expressed on the surface of hepatocytes in the liver. It promotes receptor-mediated uptake of plasma lipoproteins that harbor triglycerides and cholesterol and specifically catalyzes hydrolysis of triglycerides, actions that are suppressed when LTβR signaling is switched on. Expression of pparg was negatively affected by LTβR signaling in wt and relA-/- but not in relB-/- cells (belonging to cat II/2 genes), indicating that this gene was downregulated by RelB in response to LTβR stimulation. Our finding is a further indication that LTβR signaling represses lipogenesis and it may do so via RelB. It has been shown that ligand-induced transactivation by PPARγ is suppressed by IL-1 and TNF and that this suppression is mediated through NF-κB (p50-RelA) [36]. However, unlike suppression of PPARγ by p50-RelA, where this heterodimer blocks PPARγ binding to DNA by forming a complex with PPARγ and its co-activator PGC-2, LTβR-mediated suppression of pparg occurred via transcriptional repression executed by RelB. Further experiments are required to find out whether RelB directly or indirectly mediates repression of pparg transcription in response to LTβR signaling. The repressive effect of LTβR signaling on adipogenesis has been confirmed in MEFs that were induced for adipogenic differentiation. LTβR stimulation resulted in attenuated lipid droplet accumulation as well as in reduced pparg and adipogenic marker gene (fabp4/ap2) expression under conditions that promote differentiation into adipocytes (unpublished results).

Conclusion

This study is the first systematic dissection of the RelA- and RelB-driven transcriptome response downstream of the LTβR. We confirmed previously described LTβR-regulated genes. More importantly, we identified novel LTβR-responsive genes and assigned underlying regulatory mechanisms executed by RelA and/or RelB to them (Table 13). We found that the majority of LTβR-regulated genes required the presence of both RelA and RelB, suggesting significant crosstalk between the two NF-κB activation pathways. Gene Ontology analysis confirmed that LTβR-NF-κB target genes were predominantly involved in the regulation of immune responses. However, other biological processes such as apoptosis/cell death, cell cycle, angiogenesis, and taxis were also regulated by LTβR signaling. Furthermore, we show that LTβR stimulation downregulated expression of the gene encoding PPARγ, suggesting that LTβR signaling may repress adipogenic differentiation by attenuating the levels of this key adipogenic transcription factor. Our findings are significant since they indicate a role for LTβR signaling beyond immune responses and lymphoid organ development and assign underlying gene expression regulatory mechanisms to the LTβR transcriptome.

Methods

Cell culture

Mouse embryonic 3T3 fibroblasts (wild-type, relA-/-, and relB-/-; kind gift from A. Hoffmann) were cultured at 37°C in Dulbecco's modified Eagle's medium (GIBCO/Invitrogen, Karlsruhe, Germany) supplemented with 10% heat-inactivated bovine calf serum (Perbio Science, Bonn, Germany), penicillin (100 U/ml), streptomycin (100 μg/ml), and Glutamax I (2 mM) (GIBCO/Invitrogen) and treated with agonistic anti-LTβR mAb (1 μg/ml, clone AC.H6; kind gift from J. Browning and P. Rennert).

EMSA

Preparation of nuclear extracts and EMSAs were essentially performed as previously described [37]. Nuclear and cytoplasmic fractions were prepared according to standard procedures [38].

RNA isolation

Total cellular RNA was isolated using the RNeasy Mini Kit (Qiagen, Hilden, Germany) according to the manufacturer's instructions. Possible contamination by genomic DNA was removed by DNaseI treatment using the RNase-Free DNase Set (Qiagen). Quality of RNA samples was checked by spectrophotometry and agarose gel electrophoresis. RNAs (2 μg total RNA per sample) were used for cRNA preparation for microarrays only when the ratio A260:A280 was 1.8–2.1 and the RNA was intact.

Microarrays

Microarray analysis was performed using CodeLink UniSet Mouse 20K I bioarrays (GE Healthcare, Munich, Germany), a one-color system where for each of the investigated 19,801 transcripts there is one 30–mer oligo probe spotted per slide. For gene expression profiling, untreated (0 h) and 10 h agonistic anti-LTβR mAb treated wt, relA-/-, and relB-/- MEFs were used. For every treatment group, cells from 4 experiments were pooled, total RNA isolated, cRNA prepared and hybridized onto the bioarrays in technical triplicates. cRNA target preparation, bioarray hybridization and detection were carried out according to the manufacturer's protocol provided with the CodeLink Expression Assay Reagent Kit. For scanning microarrays, a GenePix 4000B Array Scanner and GenePix Pro 4.0 software (Axon Instruments Inc./Molecular Devices, Munich, Germany) were employed according to settings suggested by the protocol provided with the CodeLink Expression Assay Reagent Kit. Microarray data have been deposited in NCBIs GEO http://www.ncbi.nlm.nih.gov/geo/ and are accessible through GEO series accession number GSE11963.

Microarray data preprocessing

Microarray raw data of stimulated and unstimulated MEFs were analyzed using the Codelink™ Expression Analysis v4.1 software (GE Healthcare) and MDFC values were extracted. All subsequent analyses were performed using R and Bioconductor. For the analysis only genes with probe type 'DISCOVERY' were considered (19,801 genes) and all genes flagged MSR (Manufactory Slide Report) in any sample were excluded (leaving 19,580 genes). To remove negative expression values (local background > spot intensity) raw intensities with values < 0.01 were set to 0.01. The raw intensities of each array were scaled to the array median. After logarithmizing the expression values quantile normalization was applied across all arrays.

Differentially expressed genes

Array data for the different genotypes were analyzed separately. A gene was included in the analysis if it was flagged 'G' (good) or 'S' (contains saturated pixels) on at least two arrays in any of the two groups (stimulated or unstimulated). Furthermore, genes selected were required to have a FC higher than or equal to the FC threshold determined from the maximum MDFC in these groups. To identify genes significantly differentially expressed after stimulation, a Student's t-test was performed for the previously filtered genes. The resulting p values were corrected for multiple testing using the method of Benjamini and Hochberg [39]. Allowing a false discovery rate of 5%, a total of 528 genes were identified that were significantly regulated in wt cells (regardless whether they were regulated somewhere else). From these, 366 genes were regulated exclusively in wt, 30 genes in wt and relA-/-, 102 in wt and relB-/- cells and 30 genes in all 3 genotypes.

Functional analysis with GO

Analysis of functional enrichment was performed employing Fisher's exact test. The resulting p values (p < 0.01) were used to rank GO terms according to their significance. Terms with more than 600 genes on the array or less than 3 genes on the list of investigated genes were regarded as too general or too specific, respectively, and excluded from the analysis. Expert knowledge was used to assign broader themes to specific GO categories.

qRT-PCR

For qRT-PCR, first strand cDNA was obtained from 2 μg of total RNA for each treatment group using oligo-dT primers and M-MLV Reverse Transcriptase kit (Promega, Mannheim, Germany) according to manufacturer's protocols. qRT-PCRs were performed in an iCycler Thermal Cycler real-time PCR machine (Bio-Rad Laboratories, Hercules, CA) using SYBR Green I as detector dye and reagents from the Quantace SensiMix DNA Kit (Quantace Ltd., Watford, UK). Primers for qRT-PCRs with Tm of 60°C were designed using Primer3 software (v. 0.4.0; http://frodo.wi.mit.edu) [40]. For individual samples, each gene was tested in triplicates and the mean of the 3 cycle threshold values was used to calculate relative expression levels. For normalization, β-actin was used as an endogenous reference gene to correct for variation in RNA content and variation in the efficiency of the reverse transcription reaction. Statistical analysis of qRT-PCR results from 3 independent LTβR stimulation experiments was performed employing a Welch test. Forward (F) and reverse primers (R) in 5' to 3' orientation were: Nfkb2_F: GCTAATGTGAATGCCCGGAC, Nfkb2_R: CTTTGGGTATCCCTCTCAGGC, Ccl2_F: CCCACTCACCTGCTGCTACT, Ccl2_R: TCTGGACCCATTCCTTCTTG, IκBα_F: TGCACTTGGCAATCATCCAC, IκBα_R: TTCCTCGAAAGTCTCGGAGCT, Ralgds_F: CATCCACCGCCTAAAGAAGA, Ralgds_R: GGGCTCTCCTAGGGTTCATC, Cx3cl1_F: GGCTAAGCCTCAGAGCATTG, Cx3cl1_R: CATTTTCCTCTGGGGTTGA, Pparg_F: TCATGACCAGGGAGTTCCTC, Pparg_R: GGCGGTCTCCACTGAGAATA, Enpp2_F: TGGCTTACGTGACATTGAGG, Enpp2_R: GTCGGTGAGGAAGGATGAAA, Birc3_F: TGACGTGTGTGACACCAATG, Birc3_R: TGAGGTTGCTGCAGTGTTTC, Cxcl10_F: AAGTGCTGCCGTCATTTTCT, Cxcl10_R: GTGGCAATGATCTCAACACG, Irf1_F: ACCCTGGCTAGAGATGCAGA, Irf1_R: TTTGTATCGGCCTGTGTGAA, Cd74_F: ATGACCCAGGACCATGTGAT, Cd74_R: CCAGGGAGTTCTTGCTCATC, Fosl1_F: CAAAATCCCAGAAGGAGACAAG, Fosl1_R: AAAAGGAGTCAGAGAGGGTGTG, Ccl7_F: AATGCATCCACATGCTGCTA, Ccl7_R: ATAGCCTCCTCGACCCACTT, Cxcl1_F: GCTGGGATTCACCTCAAGAA, Cxcl1_R: TGGGGACACCTTTTAGCATC, Id2_F: CCCCAGAACAAGAAGGTGAC, Id2_R: ATTCAGATGCCTGCAAGGAC, β-actin_F: TGGCGCTTTTGACTCAGGA, β-actin_R: GGGAGGGTGAGGGACTTCC

Abbreviations

LTβR: 

lymphotoxin-β receptor

IκBα: 

inhibitor of NF-κB α

MEF: 

mouse embryonic fibroblasts

GO: 

Gene Ontology

PPARγ/pparg

peroxisome proliferator activated receptor-γ

TNFR1: 

tumor necrosis factor receptor 1

TLR4: 

Toll-like receptor 4

NEMO: 

NF-κB essential modulator

IKK: 

IκB kinase

NIK: 

NF-κB-inducing kinase

relA -/-

RelA-deficient

TNF: 

tumor necrosis factor

relB -/-

RelB-deficient

wt: 

wild-type

mAb: 

monoclonal antibody

EMSA: 

electrophoretic mobility shift assay

FC: 

fold change

MDFC: 

minimal detectable fold change

cat: 

category

JNK: 

c-Jun N-terminal kinase

LIGHT: 

lymphotoxin-related inducible ligand that competes for glycoprotein D binding to herpesvirus entry mediator on T cells

FDC: 

follicular dendritic cell

GC: 

germinal center

HEV: 

high endothelial venule

A/CD: 

apoptosis/cell death

CCY: 

cell cycle

IR: 

immune related

BR: 

blood vessel development related

T: 

taxis, response to external/chemical stimulus

ION: 

ion homeostasis

LY: 

hematopoietic or lymphoid organ developmental processes

qRT-PCR: 

quantitative real-time reverse-transcription PCR

IL-1: 

interleukin-1

MSR: 

Manufactory Slide Report

SD: 

standard deviation.

Declarations

Acknowledgements

We thank Jeffrey Browning and Paul Rennert for agonistic anti-LTβR mAb and Alexander Hoffmann for mouse embryonic 3T3 fibroblasts (wild-type, relA-/-, and relB-/-). We gratefully acknowledge Heike Mondrzak, Ulrike Schure, Melissa Lehmann, Kerstin Andreas, and Sandra Westhaus for technical help with the qRT-PCR. We are indebted to Hans Peter Saluz for providing us with the possibility to use the GenePix 4000B Array Scanner. We also thank Marc Riemann for valuable discussions on this manuscript.

Authors’ Affiliations

(1)
Research Group Immunology, Leibniz Institute for Age Research, Fritz Lipmann Institute
(2)
Bioinformatics – Pattern Recognition, Leibniz Institute for Natural Product Research and Infection Biology, Hans-Knöll-Institute
(3)
Molecular and Applied Microbiology, Leibniz Institute for Natural Product Research and Infection Biology, Hans-Knöll-Institute
(4)
Signal Transduction in Tumor Cells, Max Delbrück Center for Molecular Medicine
(5)
Institute for Vascular Medicine, Friedrich Schiller University of Jena
(6)
Institute for Community Medicine, Ernst Moritz Arndt University Greifswald

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© Lovas et al; licensee BioMed Central Ltd. 2008

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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