1A Multiple Sclerosis Treatment

Network analysis of transcriptional regulation in response to intramuscular interferon-b-1a multiple sclerosis treatment

M Hecker1,2, RH Goertsches2,3, Interferon-b (IFN-b) is one of the major drugs for multiple sclerosis (MS) 3 2 treatment. The purpose of this study was to characterize the transcriptional C Fatum , D Koczan , effects induced by intramuscular IFN-b-1a therapy in patients with relapsing– 2 1 H-J Thiesen , R Guthke remitting form of MS. By using Affymetrix DNA microarrays, we obtained and UK Zettl3 genome-wide expression profiles of peripheral blood mononuclear cells of 24 MS patients within the first 4 weeks of IFN-b administration. We identified 1Leibniz Institute for Natural Product Research 121 genes that were significantly up- or downregulated compared with and Infection Biology—Hans-Knoell-Institute, baseline, with stronger changed expression at 1 week after start of therapy. Jena, Germany; 2University of Rostock, Institute of Immunology, Rostock, Germany and Eleven transcription factor-binding sites (TFBS) are overrepresented in the 3University of Rostock, Department of Neurology, regulatory regions of these genes, including those of IFN regulatory factors Rostock, Germany and NF-kB. We then applied TFBS-integrating least angle regression, a novel integrative algorithm for deriving gene regulatory networks from gene Correspondence: M Hecker, Leibniz Institute for Natural Product expression data and TFBS information, to reconstruct the underlying network Research and Infection Biology—Hans-Knoell- of molecular interactions. An NF-kB-centered sub-network of genes was Institute, Beutenbergstr. 11a, Jena, D-07745, highly expressed in patients with IFN-b-related side effects. Expression Germany. alterations were confirmed by real-time PCR and literature mining was E-mail: [email protected] applied to evaluate network inference accuracy. The Pharmacogenomics Journal (2012) 12, 134–146; doi:10.1038/tpj.2010.77; published online 19 October 2010

Keywords: interferon-beta; multiple sclerosis; DNA microarray; network analysis

Introduction

Multiple sclerosis (MS) is an idiopathic inflammatory disorder of the central nervous system and the most common disabling neurologic disease of young adults. It is a lifelong disease that affects the nervous system by destroying the protective covering (myelin) that surrounds nerve fibers, thereby provoking impaired nerve conduction. Genetic susceptibility, environmental exposure and immune dysregulation are thought to have a significant role in its pathogenesis. An autoimmune basis is assumed to cause lesions in the brain and spinal cord, but it is less clear how the autoimmune processes originate and maintain.1–4 At present there is no cure for MS, and existing therapies are designed primarily to prevent lesion formation and brain atrophy, decrease the rate and severity of relapses and delay the resulting disability by reducing levels of inflammation. Interferon-b (IFN-b) is currently the most established treatment for controlling the exacerbations in relapsing–remitting MS and several studies have confirmed its clinical benefit.5–7 Received 28 January 2010; revised 27 August 2010; accepted 30 August 2010; IFN-b is a natural human pleiotropic cytokine with antiproliferative and published online 19 October 2010 immunomodulatory activities that is produced by various cell types including Transcriptional effects of IFN-b therapy in MS M Hecker et al 135

fibroblasts and macrophages. It exerts its biological effects by binding to specific cell surface receptors. This binding mk initiates a cascade of intracellular events, which leads to the 1500 activation of transcription factors (TFs) including signal B transducers and activators of transcription, IFN regulatory factors and NF-kB.8 These translocate to the nucleus and drive the expression of numerous genes that could serve as biological markers of IFN-b activity (for example, MX1, TNFSF10, B2M and IFIT1). Transcript levels of these biological response markers rapidly increase within 12 h of IFN-b administration and remain elevated for at least 3 days.9–12 Although the initial steps of the intracellular 6M 136

signaling pathways that are triggered by IFN-b have been B delineated in great detail, the mechanisms instrumental for its therapeutic efficacy in MS are still poorly understood.

It is likely that of the many (immune system) processes o measure mRNA levels. Moreover, the blood sampling that are influenced by IFN-b—directly or indirectly as a consequence of the activity of induced proteins—only a ‘) are shown. mk small proportion is responsible for the clinical benefit. This suggests a cascade of events following IFN-b administration, in which some are beneficial, others may have no effect on MS, and still others may be deleterious and cause side effects. The balance of all these effects is favorable in most MS patients and leads to reduced disease activity and lowered accumulation of disease burden. However, clinical trials demonstrated that MS patients exhibit considerable interindividual heterogeneity in their clinical course and response to IFN-b therapy. Approximately one- third of the patients suffer from a higher or identical annual relapse rate while on treatment than before (non-responders).13 At present, no established markers capable of predicting either favorable or detrimental responses to IFN-b are available.

Different IFN-b medications are available for MS. Three -1a treatment in MS b have been approved as first-line therapies for the treatment of relapsing–remitting MS in the mid-1990s: Avonex (IFN-b- 1a intramuscular (i.m.); Biogen Idec, Cambridge, MA, USA), Rebif (IFN-b-1a subcutaneous; Merck Serono, Darmstadt, Germany) and Betaferon (IFN-b-1b subcutaneous; Bayer Schering, Leverkusen, Germany). i.m. IFN-b-1a is given once a week, whereas subcutaneous (s.c.) IFN-b-1a and IFN-b-1b requires three to four injections per week. IFN-b-1a i.m. appears to be well tolerated with 4% of treated patients discontinuing injections because of adverse events in the pivotal clinical trial.6 Moreover, it has the lowest incidence 14

(approximately 2%) of neutralizing antibodies. 20072007 5 2 PBMC Whole blood UCSF Human 21k Oligo array CodeLink UniSet Human I Bioarray GSE5574 GSE5678 0 h, 24 h, 0 h, up to 7x within 1W 438 It has been repeatedly demonstrated that IFN-b-mediated 2010, 20092008, 2003 12 22 PBMC PBL Affymetrix HG-Focus GeneFilters GF211 DNA arrays Not available Not 0 available h, 8x within 1W, 0 1 h, M, 3M+10 6 h, M, 6M+10 12M h, 6M+42h 1539 gene-regulatory effects can be accessed by expression profiling of peripheral blood cells using DNA microar- 11,12 rays.15,16 In the recent past, a few high-throughput analyses . 17,18 . have been completed in an attempt to explicitly characterize et al

the global transcriptional changes in the blood that occur in et al response to IFN-b-1a i.m. (Table 1). In a pharmacogenomic 20 et al B . study by Weinstock–Guttman ., 4000 genes were 19 . measured, of which about 1500 were identified as up- or Blood expression profiling studies on intramuscular IFN- et al downregulated after the first dose and after chronic admin- et al istration of IFN-b-1a i.m.11 This showed that the therapy This studyHesse, Sellebjerg 2010 24 PBMC Affymetrix HG-U133 A and B GSE19285 0 h, 1W, 1M 121 Author Year #Patients Sample Microarray GEO accession Sampling #Genes Weinstock-Guttman Singh time points, as well as the number of genes found modulated in expression during therapy (on the basis of different filtering criteria, ‘#Genes Table 1 Abbreviations: GEO, gene expressionFive omnibus; microarray h, studies hour; have M, been month; conducted in PBL, this peripheral field blood since lymphocytes; 2003. W, The week. table provides the number of patients examined and the sample material used t induces changes in the expression of many genes Fernald

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(for example, cytokines and cell adhesion molecules), and and without side effects to the therapy. In 2007, Fernald that IFN-b has effects on multiple processes. et al.20 published the first and only study that incor- However, existing studies primarily explored the transient porated computational network inference to investigate immediate gene expression changes few hours after therapy gene regulation in response to IFN-b-1a i.m. They used onset, and to a lesser extent the (possibly indirect) regu- mutual information as a similarity measure to derive a large latory effects of IFN-b that appear at a late stage between two network of genes. subsequent injections and sustain for a longer period of Here, we used DNA microarrays to measure the transcrip- time. Moreover, most reports just list differentially expressed tional profile of peripheral blood mononuclear cells (PBMC) genes, but as genes tend to interact, it is more meaningful to from 24 MS patients within the first month of weekly i.m. arrange them in gene regulatory networks (GRNs) based on injection of IFN-b-1a. The data allowed to assess sustained expression data and known molecular interactions. A GRN, changes in expression and we analyzed the functional in principle, denotes the assembly of regulatory effects that characteristics and transcription factor-binding sites (TFBS) conduct the mutual transcriptional control of a set of genes. of the genes up- or downregulated during therapy. Using an Various modeling approaches have been proposed to integrative modeling approach we reconstructed a GRN in reconstruct GRNs from experimental data on the basis of which TFs are linked to the modulated genes to provide a different mathematical concepts and learning strategies, and deeper molecular understanding of the drug’s regulatory distinct degrees of abstraction. Novel network inference mechanisms early in therapy. We further examined whether algorithms integrate diverse types of data (for example, gene specific expression patterns relate to flu-like side effects, expression data and protein–DNA interaction data), incor- which are a common cause of early treatment failure.23 porate previous biological knowledge (for example, from scientific literature) and use specific modeling constraints to obtain more accurate GRN models.21,22 A network analysis Materials and methods could provide testable hypotheses about the drug’s mechanisms of action and may have clinical implications by Study population accentuating gene sub-networks with expression differences A total of 24 Caucasian patients (18 females/6 males, mean between responders and partial responders or patients with age 35.8 years; Table 2) diagnosed with relapsing–remitting

Table 2 Clinical and demographic characteristics of the patients

Patient ID Gender Age (years) EDSS (baseline) EDSS (3 months) Relapse during first 3 months Side effects during first 3 months

Pat01 Female 47 2.5 3.0 No No Pat02 Female 45 1.5 1.5 No Yes Pat03 Female 30 2.5 2.5 No Yes Pat04 Female 40 1.0 1.0 No Yes Pat05 Female 28 1.5 1.5 No No Pat06 Female 31 1.0 1.0 No Yes Pat07 Female 44 1.0 1.0 No Yes Pat08 Female 45 1.5 1.5 No Yes Pat09 Female 24 0.0 1.0 No Yes Pat10 Male 44 1.5 1.5 No Yes Pat11 Male 43 1.0 1.0 No Yes Pat12 Male 27 1.0 1.0 No No Pat13 Female 39 0.0 0.0 No No Pat14 Male 22 0.0 0.0 No Yes Pat15 Female 34 0.0 0.0 No Yes Pat16 Female 20 1.5 1.5 Yes Yes Pat17 Female 30 1.0 1.0 No Yes Pat18 Female 38 0.5 1.0 No Yes Pat19 Male 41 1.5 1.5 Yes Yes Pat20 Female 35 0.0 0.0 No Yes Pat21 Female 38 2.0 1.5 No Yes Pat22 Female 49 0.0 0.0 No Yes Pat23 Male 26 1.0 1.0 No Yes Pat24 Female 40 1.5 1.5 No Yes

Mean±s.d. Median (Range) Median (Range)

35.8±8.3 1.0 (0.0–2.5) 1.0 (0.0–3.0)

Abbreviations: EDSS, expanded disability status scale; s.d., standard deviation.

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MS by McDonald criteria24 were analyzed in this study. The determination of transcript levels. We used version 1.5.0 of patients were prescribed a first therapy with 30 mg once- the custom chip definition files (for HG-U133 A and B) and weekly, i.m. IFN-b-1a (Avonex; Biogen Idec). None of the the MAS5.0 algorithm to preprocess the raw probe inten- patients had previously been medicated with immuno- sities. Data normalization was performed, separately for the modulatory or immunosuppressive agents or had ever arrays of type A and B, by a loess fit to the data with received cytotoxic treatments, and all were free of gluco- span ¼ 0.05 (using R package affy). Each A- and B-chip corticoid treatment for at least 30 days before blood yielded mRNA abundances of 11220 and 6771 human genes, extraction. Patients were assessed neurologically and rated respectively. For the 2257 genes that were measured with using the expanded disability status scale at regular inter- both chip types, we used the signal intensities of the A-chip. vals. The study was approved by the University of Rostock’s ethics committee and carried out according to the declara- Filtering differentially expressed genes tion of Helsinki. To identify genes substantially up- or downregulated within the first month of therapy, we used a combination of two Gene expression analysis using microarrays criteria: a test of statistical significance and a fold change With informed consent, 15 ml peripheral venous ethylene- variant. First, we analyzed which genes showed significant diaminetetraacetic acid blood samples were taken from all expression changes by use of a paired t-test comparing patients immediately before first, second and fifth IFN-b the baseline expression levels with the levels at 1 week and injection, that is, at baseline as well as 1 and 4 weeks post 4 weeks into therapy. To further narrow down the filtering therapy initiation. The samples were always collected result, we evaluated signal intensity-dependent fold changes at the same time of the day and processed within 1 h. (MAID filtering)26 (http://www.hki-jena.de/index.php/0/2/ Total RNA of Ficoll-isolated PBMC from each sample 490). In this way, we took into account that the variability was extracted following manufacturer’s protocol (RNeasy; in the (log) fold changes increases as the measured signal Qiagen, Hilden, Germany). We used PBMC instead of whole intensity decreases.27 The absolute value of a gene’s MAID blood to reduce interferences of globin RNA and thus score is higher, the more its expression level is altered after increase the sensitivity of the microarray hybridization start of therapy, and it is generally lower for weakly- results. Initial RNA and final complementary RNA concen- expressed genes. Finally, we selected the protein-coding trations were determined spectrophotometrically by a genes having |MAID score|43.0 and t-test P-valueo0.01 as Nanodrop 1000 (Thermo Fisher Scientific, Waltham, MA, IFN-b-responsive genes. USA) and quality control was performed by native ethidium To provide an estimate of the number of genes passing the bromide agarose gel electrophoresis. Samples of 7 mg total filtering by chance, a permutation test was performed. The RNA were labeled and hybridized to Affymetrix (Santa data set was permutated 1000 times by randomly rearran- Clara, CA, USA) human genome U133 A and B arrays in ging the temporal sequence of the data of each patient. The accordance with the supplier’s instructions. The arrays were same filtering criteria as described above were applied to scanned at 3-mm resolution using the Hewlett Packard each permutation. GeneArray Scanner G2500A (Affymetrix). The raw data In addition, we analyzed the genes with significant were stored according to the MIAME standard and are expression changes to identify a subset of genes whose available from Gene Expression Omnibus (accession number expression correlates with IFN-b-related side effects. For this GSE19285, http://www.ncbi.nlm.nih.gov/geo/). purpose, we compared the baseline transcript levels of patients with and without flu-like symptoms using a two- Validation of the microarray data by real-time PCR sided two-sample t-test with the significance level at Quantitative real-time reverse transcription PCR was used to a ¼ 0.05. confirm changes in gene expression revealed by the DNA microarray experiments. We measured the expression levels Gene ontology (GO) analysis of 15 selected genes in a subset of the samples. Details on the We examined the set of IFN-b-responsive genes for over- experimental procedure, as well as on the analysis of the represented GO terms to assess their functional character- real-time PCR data are described in Supplementary Materials istics. Using GOstats, a software package written in R,28 each and Methods. GO term was tested whether it is significantly associated to the list of filtered genes, that is, whether it is related with Microarray data preprocessing higher frequency to these genes than to the 15734 genes In the applied Affymetrix microarrays most probesets measured in total. The analysis was performed for all gene include probes matching transcripts from more than one functional annotations of the biological process GO cate- gene and probes that do not match any transcribed gory provided by the Bioconductor annotation package sequence. Therefore, we utilized custom chip definition org.Hs.eg.db version 2.2.6 (http://www.bioconductor.org). files that are based on the information contained in the GeneAnnot database version 1.825 (http://bioinfo2.weizmann. TFBS analysis ac.il/geneannot/). GeneAnnot-based chip definition files are To reconstruct the regulatory interactions between the genes composed of probesets including exclusively probes with expression changes in response to IFN-b, we applied a matching a single gene and thus allow for a more reliable GRN inference algorithm that integrates information on

The Pharmacogenomics Journal Transcriptional effects of IFN-b therapy in MS M Hecker et al 138

TFBS as previous knowledge. Evolutionarily conserved TFBS subset of the putative TF–gene interactions obtained from were derived from the tfbsConsSites track of the UCSC TFBS analysis. As TILAR not necessarily employs all those database build hg18 (http://genome.ucsc.edu). The track TF–gene interactions, the method considers the fact that data were generated using position weight matrices of TFBS they are predicted and therefore not all of them might refer contained in the public Transfac database (version 7.0, http:// to biologically functional binding sites. The regression www.gene-regulation.com/cgi-bin/pub/databases/transfac/). coefficients (that is, the nominal parameters in the model) For the whole human genome 3837187 TFBS predictions to be estimated by least angle regression30 then specify the associated to 258 different position weight matrices can be presence and strength (edge weights) of each possible gene– retrieved from the tfbsConsSites track (as of 19 November TF interaction. The final model was determined by 10-fold 2009). With this at hand, we screened the regulatory region cross-validation (to avoid overfitting to the data), visualized of each of the 15734 measured genes for TFBS occurrences. with Cytoscape 2.6.0,31 and tested for scale-freeness accord- The regulatory region was specified as the genomic sequence ing to Clauset et al.32 For a more complete description of 1000 bp up- and downstream of the transcription start the modeling approach, we refer to our original paper on site stated in the GeneCards database version 2.39 (http:// TILAR.26 www.genecards.org). Furthermore, to take into account the The major advantage of this inference technique is that inherent redundancy of the Transfac database, we grouped only few model parameters are sufficient to define a very similar or identical sequence motifs by use of STAMP.29 complex network, which is still readily interpretable in In this way, we could reduce the 258 Transfac position weight terms of true molecular interactions. Moreover, TF expres- matrices to 101 distinct DNA-binding patterns. Using a sion levels are not required for the network reconstruction, hypergeometric test, we identified a subset of (consolidated) as the activity of TFs is modeled implicitly. This is beneficial, TFBS motifs overrepresented at the significance level a ¼ 0.1. as TF expression seldom correlates with TF activity. We That means, these motifs are enriched in the regulatory evaluated and compared the performance of TILAR (inclu- regions of the filtered genes when compared with the ding the adaptive variant) and five different GRN inference distribution of TFBS within the promoter sequences of all methods using literature mining information. A detailed measured genes. This information corresponds to a list of report on this benchmarking analysis can be found in predicted TF–gene interactions that can serve as a template Supplementary Materials and Methods. We supply R codes for inferring the GRN. A TF–gene interaction represents a for TILAR at our institute’s website (http://www.hki-jena.de/ physical interaction, that is, a TF (or a group of TFs with index.php/0/2/490). similar binding specificity) binds at least once the DNA at the region that encompasses the transcription start site of a gene Results and thus presumably regulates its transcription. Patient characteristics Integrative GRN modeling The demographic and clinical characteristics of the 24 We applied the TFBS-integrating least angle regression patients in our study are summarized in Table 2. A total of (TILAR) algorithm to construct a GRN model of IFN-b-res- 20 patients had systemic side effects in form of flu-like ponsive genes. TILAR is a novel method for inferring GRNs symptoms (including fever, asthenia, myalgia and head- from gene expression data, incorporating known or pre- aches) within the first 3 months on treatment. Two dicted TFBS and, if available, literature mining information patients experienced a relapse in this observation period. 26 (adaptive TILAR). The modeling approach distinguishes None showed injection-site reactions or liver function two types of network nodes: genes (that were selected for abnormalities. identifying the regulatory interactions between them) and TFs (whose binding sites are overrepresented in the Transcriptional changes in response to IFN-b therapy regulatory regions of the genes). The algorithm then assigns The preprocessing of the microarray data resulted in an (directed) TF–gene and gene–TF interactions (network edges) expression data set of 15734 different genes and 72 PBMC by fitting a system of linear equations to the genes’ samples. By filtering for genes significantly up- or down- expression levels. In comparison with TF–gene interactions, regulated after IFN–b–1a i.m. therapy onset, we detected gene–TF interactions can have different meanings, for gene expression changes that were common among the example, the gene itself might encode a transcriptional patients. We identified 102 genes as differentially expressed regulator of the TF, or the gene product controls (possibly at week 1 versus baseline (Figure 1), and 24 genes at week 4 through a signaling cascade) the activity of the TF at versus baseline. Altogether, 72 genes were found upregu- the proteome level. Using both types of interactions, the lated and 49 genes downregulated, comprising a set of 121 modeling considers that genes usually regulate other genes filtered genes in total (Supplementary Table 1). None of our indirectly through the activity of one or more TFs. patients showed an apparent spontaneously increased type I The actual GRN inference problem is formulated as a interferon activity before treatment, which is a phenomen- linear regression equation that satisfies specific constraints on observed in a subgroup of untreated patients in other on the network structure: The model is constrained to be studies.17,18 sparse reflecting that genes are regulated by a limited The permutation test disclosed that the number of 121 number of regulators. Moreover, TILAR includes only a differentially expressed genes is significantly higher than

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Figure 1 Clustering analysis and graphical representation of the data. Heatmap of 102 genes identified as up- (60 genes, gray labeled rows) or downregulated (42 genes, green) one week after first intramuscular injection of IFN-b-1a. Hierarchical clustering was performed on the basis of the complete linkage method and Pearson’s correlation coefficient as a measure of similarity. Signal intensities were centered and scaled row-wise (yielding z-scores) for visualization purposes. Despite a strong interindividual variability, the clustering tends to separate baseline measurements (green labeled columns) from gene expression levels obtained at 1 week into therapy (gray). The data of only few patients do not cluster according to the time points, for example, for patient 16 (the two leftmost columns), who already showed at baseline a relatively high expression of some genes generally found upregulated after 1-week therapy. The row labels of the heatmap, that is, the gene symbols, are given in Supplementary Table 1. Exemplary, the dynamics of five down- and two upregulated genes are shown on the right. The y-axis is the average expression level of the 24 patients, and error bars indicate standard errors. *P-valueo0.005, þ P-valueo0.05. would be expected by chance. When randomly shuffling the IFN-inducible protein involved in the innate immune sampling time points for each patient for 1000 times and response to viral infection.33 The water-selective membrane analyzing each permutated data set for modulated genes, channel AQP9 is thought to have a role in the immunolo- between 8 and 89 genes were filtered (on average 21.4). gical function of leukocytes.34 FCER1A is an Fc receptor for Hence, the number of filtered genes was below 121 in each IgE molecules, which after crosslinking can lead to induced permutation, which implies an empirical P-value for the NF-kB activation in monocytes and thus cytokine produc- actual filtering result of o0.001. This shows that (most of) the tion.35 Apart from that, 10 of the genes in the category identified messenger (m)RNA changes are due to the therapy. ‘positive regulation of immune response’ constitute a The overall patterns of gene expression revealed by real- subgroup of genes annotated with the GO term ‘B cell- time PCR were similar to those obtained using microarrays. mediated immunity’ (GO:0042221). The mRNA measurements of both techniques correlated G proteins (GNAZ and GNG8) and G protein-coupled significantly for all 15 remeasured genes. When comparing receptors (GPR20, GPR44, GPR56 and GPR97) also showed the expression levels before first and second IFN-b injection, significant expression changes in response to the therapy. real-time PCR analysis also confirmed the significance of Modifications in the activity of GPRs characterize lympho- expression changes for all of these genes. Full results of the cytes from MS and other chronic immune disorders, and real-time PCR experiments are presented in Supplementary it is assumed that IFN-b-1a affects the expression of GPR Materials and Methods. regulatory proteins.36 Besides, we noted an upregulation of specific cell adhesion molecules (AM) including the integ- Functional analysis of IFN-b-responsive genes rins (JAM3, ESAM, CLDN5, ITGA2B and ITGB3). Within the The result of the GO analysis (Table 3, Supplementary Table 1) immune system, AM mediate cell contacts and bring shows that most of the filtered genes are known to leukocytes to sites of inflammation or injury, and thus they participate in immune system processes. Genes associated may be important in MS brain lesions4 (see Discussion). to ‘positive regulation of immune response’ are significantly enriched in the gene set. The corresponding GO category Comparison with other expression profiling studies on (GO:0050896) comprises 26 out of the 121 genes, inclu- IFN-b-1a i.m. ding TNFSF10, TLR7, FCER1A, IL1R2, OAS1 and AQP9. These Several of the 121 filtered genes have already been described genes have quite diverse immune functions. OAS1, a as genes altered at the transcript level in response to i.m. member of the 2–5A synthetase family, is an essential IFN-b-1a therapy. In the study by Fernald et al.,20 immune

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Table 3 Overrepresented terms of the GO biological process ontology

GO BP ID GO Term P-value Expected count Count

GO:0006955 Immune response o0.0001 3.89 14 GO:0002376 Immune system process 0.0001 5.34 16 GO:0009605 Defense response 0.0005 4.47 13 GO:0030334 I-kB kinase/NF-kB cascade 0.0019 0.53 4 GO:0009611 Positive regulation of immune system process 0.0025 2.92 9 GO:0050896 Positive regulation of immune response 0.0026 15.03 26 GO:0051270 Protein kinase cascade 0.0028 0.59 4 GO:0007218 Inflammatory response 0.0028 0.59 4 GO:0051046 Regulation of immune system process 0.0030 0.60 4 GO:0051047 Regulation of immune response 0.0034 0.30 3 GO:0042108 Positive regulation of multicellular organismal process 0.0036 0.31 3 GO:0006954 Fatty acid metabolic process 0.0046 2.07 7 GO:0006916 Immunoglobulin mediated immune response 0.0062 1.16 5 GO:0042221 B cell mediated immunity 0.0078 4.11 10 GO:0051050 Activation of immune response 0.0086 0.42 3 GO:0009967 Lymphocyte mediated immunity 0.0090 1.28 5

Abbreviation: BP, biological process. P-values were computed for each GO term based on the hypergeometric distribution. Only functional categories with P-valueo0.01 and where at least 3 out of the 121 IFN-b-responsive genes are associated (‘Count’) are shown. ‘Expected count’ gives the expected number of genes affiliated to each tested term in an arbitrary equally sized list of genes. The table lists the most representative GO terms first, i.e. those that are most characteristic for the set of filtered genes. system process (GO:0002376) genes OAS1, CST7, SNCA and that the genes share common regulatory regimes. These 11 CSF1R were found modulated by the treatment, too. Their TFs connect 77 out of the 121 genes through 152 TF–gene study focused on the fast blood expression changes within interactions, and each TF is linked to at least eight genes two days after drug administration. CSF1R, which was (Table 4). This information was used as a template for downregulated after 1 and 4 weeks into therapy in our inferring the underlying GRN by TILAR. As a reminder, each study, is a receptor for colony stimulating factor-1, a TF–gene interaction is only a potential one, as this analysis cytokine that controls differentiation and function of was based on computationally predicted TFBS because of monocytes and macrophages.37 The downregulation of the lack of experimentally determined TFBS in biological CSF1R was confirmed by real-time PCR analysis (Supple- databases. mentary Materials and Methods). In another study, Singh et It is also important to note that some TFs with highly al. examined five MS patients before and 24 h after initial similar DNA-binding properties were grouped to one TF therapeutic dose of IFN-b-1a i.m. and identified a set of 132 entity, for example, the interferon regulatory factors (IRF)-1 differentially expressed genes.19 At least eight of these genes and 2. IRF1 and IRF2 are structurally similar and bind to also occur in our filtering result: TNFSF10, OAS1, JUP, AQP9, the same regulatory elements. However, they have distinct FGFBP2, MS4A4A, TYMP and FAM26F. The mRNA levels of or even antagonistic functions. IRF2 is a repressor that TNFSF10 are known to be increased in PBMC from MS competitively inhibits the IRF1-mediated transcriptional patients compared with healthy controls38 and even have activation of interferons and IFN-inducible genes.41 been reported to be predictive of clinical responsiveness to A subset of 21 genes possess at least one TFBS for IFN-b.39 TNFSF10 encodes a cytokine of the tumor necrosis NF-kB (NFKB1, NFKB2, REL, RELA). NF-kB proteins are key factor ligand family that induces apoptosis and activation of regulators in the transcription of many inflammatory genes NF-kB. Regulation of TNFSF10 function takes place at the and are activated by various intra- and extracellular stimuli, level of receptor expression, whereas decoy receptors such as for example, cytokines like IFN-b. They can be found in TNFRSF10C can inhibit TNFSF10 from binding to tumor numerous cell types that express cytokines, cell AM and necrosis factor receptors capable of mediating apoptosis.40 acute phase proteins. We found TNFRSF10C significantly upregulated after first injection of IFN-b-1a i.m., but the expression returned to Integrative network modeling baseline levels at the 4 weeks time point. On the basis of the gene expression data (121 genes, 72 samples) and the predicted TF–gene interactions, we con- Identification of putative TF-gene interactions structed a GRN model using the TILAR algorithm. The Genes responsive to IFN-b-1a are under control of TFs, modeling was constrained to use only a subset of the whose activities are (indirectly) affected by the drug. There- putative TF–gene interactions. Here, 102 out of the 152 fore, we analyzed the genes’ regulatory regions for occur- predicted TF–gene interactions were included into the rence of overrepresented TFBS. Conserved binding sites were model. Overall, 28 nominal model parameters were set found enriched for 11 consolidated TFs, which indicates nonzero specifying the presence of gene–TF interactions.

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Table 4 TFBS overrepresented in the regulatory regions of up- and downregulated genes

TF Symbol Transfac accession Official full name P-value Expected count Count

SRF M00152, M00186, M00215 Serum response factor (c-fos serum response 0.003 8.22 17 Element-binding transcription factor) TFCP2 M00072 Transcription factor CP2 0.019 5.37 11 TOPORS M00480 Topoisomerase I binding, arginine/serine-rich 0.019 5.40 11 HOXA9, MEIS1, M00418, M00419, M00420, M00421 Homeobox A9, Meis homeobox 1, 0.022 12.46 20 TGIF1 TGFB-induced factor homeobox 1 HNF1A M00132, M00206 HNF1 homeobox A 0.040 3.85 8 TBP M00216, M00252, M00471 TATA box binding protein 0.081 6.84 11 IRF1, IRF2 M00062, M00063 Interferon regulatory factor 1 and 2 0.086 5.33 9 ZIC1, ZIC2, ZIC3 M00448, M00450, M00449 Zic family members 1–3 0.091 9.48 14 MEF2A M00006, M00232, M00231, Myocyte enhancer factor 2A 0.094 7.86 12 M00233, M00026 TFAP4 M00175, M00176, M00005 Transcription factor AP-4 (activating 0.097 13.01 18 enhancer Binding protein 4) NFKB1, NFKB2, M00051, M00052, Nuclear factor of kappa light polypeptide 0.098 15.68 21 REL, RELA M00053, M00054, M00194, gene Enhancer in B-cells family members M00208 R ¼ 152

Abbreviation: TF, transcription factor. Evolutionarily conserved TFBS of 11 TF entities were found enriched. The list includes IRFs and NF-kB, which are known TFs in IFN-b signaling.8 Highly similar Transfac motifs were consolidated before calculating the P-values. The column ‘Count’ shows the number of genes that possess a DNA-binding site for the respective TF. For instance, 9 out of the 121 genes have a potential TFBS for IRF1/IRF2 in their promoter region, which is significantly more than expected by chance (‘Expected Count’ is 5.33). In sum, there are 152 predicted TF-gene interactions.

Only one of those edges (ESAM- TFAP4) received a negative weight and hence describes a repressing effect. The final model thus consists of 28 inferred gene–TF interactions and 102 TF–gene interactions, and 72 of the 121 genes are connected to at least one TF (Figure 2, Supplementary Materials and Methods, Cytoscape session file). Network inference by TILAR outperformed all other algorithms tested in the benchmarking analysis described in more detail in Supplementary Materials and Methods. TILAR allowed for a higher prediction accuracy than using just gene expression data or TFBS information alone, and performed best when incorporating text-mining information as well (adaptive TILAR). Therefore, we confirmed that the integrative modeling strategy is able to reconstruct GRNs more reliably than other established methods.

Network characteristics The inferred network is fairly complex but still readily interpretable because of the intuitive linear modeling Figure 2 Gene regulatory network inferred by TILAR. Gene expression scheme. Each network node is under control of only few data and TFBS predictions were utilized to build a GRN model of 102 regulators rendering the network sparse. The maximum in- TF–gene and 28 gene–TF interactions. The network comprises 72 of the degree in the GRN is five (on average 1.57). Nevertheless, 121 genes that were found up- or downregulated during 1-month some nodes (in particular TFs) are highly connected in the therapy. The remaining genes are not shown here. Two parts of the network, for example, the NF-kB complex with an out- network are presented in detail in Figures 3a and b. The full model including edge weights and complete gene information is available as a degree of 15 (Figure 3b). Overall, most of the nodes are lowly Cytoscape session file in Supplementary Materials and Methods. connected and only a few are relatively highly connected. The node degrees are roughly distributed according to a power law (scale-free topology), with the fraction P(k)of A closer look at the interactions in the model revealed nodes in the network having k connections being estimated gene sets co-regulated by a common TF (Figure 3a). In the to follow P(k)BkÀ2.16. network, nine genes are under transcriptional control of

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Figure 3 Detail views of the GRN model shown in Figure 2. (a) Network inference by TILAR takes into account that TF target genes are often co-expressed. All but one of the genes that are regulated by TFCP2 is upregulated after therapy initiation. Similarly, a set of downregulated genes is predicted to contain DNA-binding sites for ZIC family members in their regulatory regions. Both TFs receive multiple regulatory inputs. (b) Some of the filtered genes have potential relevance to treatment-related adverse effects. The figure on the right shows a sub-network of genes that are highly expressed at baseline in patients experiencing side effects. According to the model, three of these genes are regulated by NF-kB, a main transcriptional regulator in the network. Outer parts are shown with lower opacity.

TFCP2, of which all but one was upregulated after first week of IFN-b-1a i.m. administration (for example, ALOX12, GPR97 and CMTM5). Similarly, TF node ZIC1|ZIC2|ZIC3, which represents a group of ZIC family zinc finger proteins, is linked to seven genes. Of these, six genes showed lowered mRNA levels in the patients’ PBMC 1 week post therapy initiation in comparison with baseline (for example, JUP, FAM26F and EGR2). The TFs ZIC1, ZIC2 and ZIC3 were grouped during TFBS analysis as they bind the same or at least very similar DNA sequences.42 The GRN model contains four regulatory feedback loops and 25 feedforward loops of minimal length (that is, just four edges) as determined by an exhaustive search. An exemplary feedback loop in the reconstructed network is the regulatory chain ‘NF-kB-EHD3-TFCP2-ALOX12- NF-kB’ (Supplementary Materials and Methods, Cytoscape session file). EHD3 and ALOX12 were both found upregulated immediately before second IFN-b injection. Figure 4 Expression differences between patients with and without side effects. Of the 121 filtered genes, six genes were found to be Patients with and without flu-like symptoms within the significantly highly expressed (t-test P-valueo0.05) before treatment in first 3 months of IFN-b therapy showed significant differ- patients suffering from flu-like symptoms early in therapy. Bars indicate ences in the transcript levels of the IFN-b-responsive genes. mean and standard error. There were six genes that not only displayed elevated amounts of mRNA after 1-week therapy with IFN-b-1a i.m., but that were also highly expressed before start of treatment in the group of patients with side effects (Figure 4, It should further be noted that four out of the five genes Supplementary Table 1). Interestingly, five of these genes in the sub-network (all but ANKRD9) showed at least a trend and two TFs (NF-kB, TOPORS) form a regulatory sub- (t-test P-valueo0.10) for higher expression in patients with network (Figure 3b). DNA-binding sites for NF-kB were flu-like symptoms not only at baseline, but also at week 1 found in the regulatory region of three of these genes. Our and 4. That means, the differences in basal expression GRN inference result thus suggests that NF-kB regulates maintained, even when the genes were generally upregu- genes associated with adverse effects of IFN-b therapy. lated 1 week after drug injection in both patient groups.

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Discussion (GO:0050896) OAS1, JUP and TNFSF10 have been described as upregulated in PBMC of MS patients receiving IFN-b-1b We analyzed transcriptional changes in response to 30 mg s.c. for at least 6 months.45 These three genes were lower once-weekly, i.m. IFN-b-1a treatment in blood cells of MS expressed at 1 week versus baseline in our data, which might patients. For this purpose, PBMC were isolated, which be explained by the weekly drug injection and blood comprise mainly lymphocytes (that is, B, T and NK cells) sampling: As mentioned above, these genes are known and monocytes, but also some thrombocytes that remain to be upregulated after a few hours, but their expression after Ficoll separation. A set of 121 genes was found returns to pre-treatment values after a few days.10,44 significantly altered in the PBMC during the first 4 weeks Possibly, mechanisms that actively counter-regulate the of drug administration. In this set, GO analysis revealed transiently increased expression of OAS1 and other early an overrepresentation of immunologically relevant genes IFN-induced genes might even lead to week-1 mRNA levels (Table 3). The impact of IFN-b therapy on gene expression that are reduced compared with baseline. Besides, we was greater after 1 week of therapy than after 1 month. This observed an elevated expression of ESAM in agreement with is compatible with results of similar studies11,19 and possibly the results of Annibali et al.46 In comparison with our study suggests a homeostatic response to the drug by mechanisms they used microarrays to obtain long-term expression that counter-regulate some changes in expression. From a profiles of PBMC from 7 MS patients receiving IFN-b-1a statistical perspective, however, the longer-term therapeutic subcutaneously. We recently published a pharmacogenomic effects may also be diluted by variations in the patients’ life study where we applied Affymetrix microarrays to measure style, nutrition, co-medication and so on. On the basis of the PBMC expression levels of 25 patients treated with the expression data, we further determined the putative TFs IFN-b-1b s.c.47 In this data set, FCER1A was the only gene mediating the gene regulatory effects of IFN-b and con- consistently downregulated over the whole observation structed a GRN model. Subsequent network analysis identi- period of 2 years. FCER1A was also found significantly fied ‘network motifs’, that is, frequent interaction patterns decreased after start of IFN-b-1a i.m. therapy in the present such as feedback and feedforward loops. A sub-network of work. Its downregulation was confirmed by real-time PCR genes was found more active in patients reporting flu-like experiments in both studies. We thus conclude that FCER1A symptoms, demonstrating that the inferred network could is generally repressed by IFN-b treatment. reflect clinical heterogeneity. According to our data, IFN-b therapy induces the expres- Studies on pharmacogenomic effects of IFN-b in PBMC sion of AM. Changes in the composition of AM in the often differ in their experimental design: Gene expression peripheral blood of MS patients receiving IFN-b have already changes have been investigated in response to different IFN- been discussed in the literature.48,49 We found significantly b treatment regimes (IFN-b-1a i.m. and s.c., as well as IFN-b- increased levels of the two integrins ITGA2B and ITGB3 at 1 1b s.c.), sometimes pooling patients receiving any of these week into therapy. They are known to form a complex therapies.15 We examined a relatively large homogeneous (glycoprotein IIb/IIIa), which mediates aggregation of cohort of relapsing–remitting MS patients (n ¼ 24, expan- activated platelets by acting as an integral membrane ded disability status scale baseline range 0–2.5) who all were receptor for fibrinogen and other ligands.50 Hence, this treated with IFN-b-1a i.m. at standard dose so that the complex has a crucial role in coagulation by plugging the findings are not distorted by differences in form, dose and endothelial layer of damaged blood vessels. Elevated route of drug administration. Apart from that, the observa- amounts of glycoprotein IIb/IIIa potentially have beneficial tion period explored by other groups ranges from a few effects in MS, where the blood–brain barrier is disrupted. hours until years into therapy (see also Table 1). The Upregulated ESAM encodes a cell-surface protein that also dynamics during the first hours have been studied particu- localizes to the junctions between adjacent platelets and is larly intensively. It was shown that changes in expression thought to limit the stability of platelet contacts so that the can be seen within 2–4 h after drug administration.9–12 optimal hemostatic response occurs following vascular Among the early IFN-b-induced genes are MX1, B2M, injury.51 ESAM further mediates homophilic adhesion of TNFSF10 and OAS1. Their mRNA and protein levels decline other expressing cells, has been linked to integrity and to baseline between 96 and 144 h after IFN-b administra- formation of blood vessels,52 and is under control of NF-kB tion.10–12,43,44 Hence, once-a-week dosing of IFN-b-1a i.m. in the inferred GRN. ESAM is related to junctional adhesion does not maintain these genes above baseline throughout molecule (JAM) family members, of which JAM3 was also a week. In our study, blood samples were drawn from the found to be highly expressed at week 1 versus baseline. patients immediately before first, second and fifth i.m. JAM3 is produced by and regulates adhesion between injection of IFN-b-1a. The data revealed the genes that are leukocytes, platelets and endothelial cells, and is involved affected by the therapy even 1 week after last injection— in the migration of leukocytes across the endothelium presumably indirectly as a consequence of the activity of during inflammation.53 However, it is difficult to specify the early IFN-induced genes—and therefore unveil the more true effect or cause of JAM upregulation after IFN-b injection persistent and late changes to the transcriptome. as for example, we cannot break down the measured Some of the 121 filtered genes have been mentioned expression levels to a certain cell type: one possibility is in related studies on other IFN-b treatment regimes. For that JAM3 is primarily upregulated in platelets, where it can instance, the general immune response regulators function as a counter-receptor for the leukocyte integrin

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Mac-1.53 This way, it could inhibit Mac-1-dependent are treated with IFN-b.61 However, the true significance and adherence of neutrophils and monocytes and their transen- clinical relevance of the detected differential gene expres- dothelial migration at the blood–brain barrier in MS sion in patients with and without side effects and the patients. A second possible mechanism might be that potential roles of NF-kB and HNF1A remain to be further the treatment stabilizes the blood–brain barrier and thus examined. stops a gradual loss of immune cells which migrate from To better understand the pharmacologic effects of IFN-b in the blood into the central nervous system.48 Activated MS, it is crucial to unravel the regulatory interaction struc- PBMC that stay in the blood then could lead to a relative ture of genes responsive to the immunotherapy. According increase in the expression of AM including JAM3. However, to the GRN inferred by TILAR, IFN-b mediates immune many open questions remain on whether and how regulation through diverse TFs including some so far un- these transcriptional changes of AM exactly contribute to recognized in MS research. The inferred network reflects therapeutic effects. many fundamental properties that constitute a GRN inclu- Evidently, the broad effects of IFN-b are not purely anti- ding sparseness, scale-freeness, decentralization, self-regula- inflammatory and beneficial. IFN-b treatment in relapsing– tion (feedback) and co-regulation. Moreover, the model remitting MS can frequently induce systemic side effects reveals network regions, for instance genes downregulated such as flu-like symptoms with fever. In contrast to changes by ZIC family members and a highly interconnected NF-kB in magnetic resonance imaging and relapse rate, a number (Figure 3), and thus provides testable hypotheses about the of adverse effects emerge at the early phase after therapy drug’s mechanisms of action. initiation. They can lessen over time, but a considerable To date, our knowledge on the biological processes amount of patients suffers from persistent or recurring side underlying MS and the efficacy and safety of available effects which may cause them to cease the treatment.23 medications is still limited. Individualized treatment and To improve the clinical success of therapy an individualized monitoring strategies are necessary to use them in a more tolerability management may be supported by molecular cost-effective way. An improved pharmacodynamic, maybe markers, but studies in this field are rare. Montalban et al.54 even cell type-specific understanding at the transcript level supposed that patients who develop fever during IFN-b should help to assess and individually optimize MS treat- therapy generally have increased levels of interleukin-6. In ments with IFN-b. Once established, this would protect the our study, some IFN-b upregulated genes were found to be patients against unnecessary drug exposure and side effects. significantly highly expressed at baseline in the group of MS Several IFN-b-responsive genes with potential prognostic patients with flu-like symptoms in the first 3 months value have already been discussed in the literature,39,45,62 of medication (ESAM, ALOX12, ELOVL7, ANKRD9, GNAZ, and we intend to scrutinize the use of such predictors on our SLC24A3). The aforementioned ESAM promotes intercellu- data once we have long-term clinical information for our lar adhesion, but it was also speculated that increased levels patients. In this study, we described sustained PBMC gene of such AM as markers of immune activation might be expression changes during first month of IFN-b-1a i.m. associated with the side effects of IFN-b treatment, in administration and derived the regulating TFs. We identified particular flu-like symptoms.48 The lipoxygenase enzyme a network region of genes associated to therapeutic side ALOX12 is present in leukocytes and thrombocytes, and effects and linked to NF-kB activity. A potential regulatory synthesizes leukotrienes (12(S)-HETE).55,56 Leukotrienes are feedback loop with NF-kB has been found. FCER1A, which is bioactive lipids that have been implicated in allergic and known to induce NF-kB activation,35 was affirmed as one of inflammatory reactions. They are potent effectors of few genes significantly repressed by IFN-b. To conclude, we hypersensitivity responses and can cause (flu-like) symp- showed that network analysis integrating different types of toms such as bronchoconstriction and increased secretion of biological data could provide new insights into treatment- mucus.57 Specific studies on 12(S)-HETE further revealed affected processes, and expose expression differences multiple functions, for example, modulation of integrin between the patients of possible clinical relevance. expression56 and stimulation of angiogenesis.55 Besides, the observed upregulation of ALOX12 in response to IFN-b is Conflict of interest remarkable as in the animal model of MS ALOX5- and ALOX15 deficiency both confer a significantly worse disease Professor Dr Zettl has received research support, as well as 58 course. For the other differentially expressed genes, it speaking fees from Bayer, Biogen Idec, Merck Serono, Sanofi should only be mentioned that, like ALOX12, ANKRD9 and Aventis and Teva. Mr Hecker, Dr Goertsches, Mr Fatum, ELOVL7 are also thought to be involved in lipid metabo- Dr Koczan, Prof Dr Thiesen and Dr Guthke declare no 59,60 lism. Interestingly, NF-kB occurs as a regulator of a potential conflict of interest. subset of these genes in the reconstructed GRN and its activity therefore potentially correlates with treatment- Acknowledgments related flu-like symptoms as well (Figure 3b). Apart from that, HNF1A is connected to seven genes in the network. We deeply thank Peter Lorenz for helpful discussions and our lab This TF is required for the expression of several liver-specific assistants Gabriele Gillwaldt, Silvia Dilk, Ina Schro¨der and Ildiko´ genes, and we hypothesize that its involvement coincides To´th for their help in performing the experiments. We are with liver function abnormalities common in patients who also grateful to study nurse Christa Tiffert for her invaluable

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