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Interferon-beta therapy in multiple sclerosis: the short-term and long-term effects on the patients’ individual gene expression in peripheral blood

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Abstract

Therapy with interferon-beta (IFN-beta) is a mainstay in the management of relapsing–remitting multiple sclerosis (MS), with proven long-term effectiveness and safety. Much has been learned about the molecular mechanisms of action of IFN-beta in the past years. Previous studies described more than a hundred genes to be modulated in expression in blood cells in response to the therapy. However, for many of these genes, the precise temporal expression pattern and the therapeutic relevance are unclear. We used Affymetrix microarrays to investigate in more detail the gene expression changes in peripheral blood mononuclear cells from MS patients receiving subcutaneous IFN-beta-1a. The blood samples were obtained longitudinally at five different time points up to 2 years after the start of therapy, and the patients were clinically followed up for 5 years. We examined the functions of the genes that were upregulated or downregulated at the transcript level after short-term or long-term treatment. Moreover, we analyzed their mutual interactions and their regulation by transcription factors. Compared to pretreatment levels, 96 genes were identified as highly differentially expressed, many of them already after the first IFN-beta injection. The interactions between these genes form a large network with multiple feedback loops, indicating the complex crosstalk between innate and adaptive immune responses during therapy. We discuss the genes and biological processes that might be important to reduce disease activity by attenuating the proliferation of autoreactive immune cells and their migration into the central nervous system. In summary, we present novel insights that extend the current knowledge on the early and late pharmacodynamic effects of IFN-beta therapy and describe gene expression differences between the individual patients that reflect clinical heterogeneity.

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Abbreviations

APC:

Antigen-presenting cell

BBB:

Blood–brain barrier

CDF:

Chip definition file

CNS:

Central nervous system

DBD:

DNA-binding domain

ECM:

Extracellular matrix

EDSS:

Expanded Disability Status Scale

GAS:

Interferon-gamma-activated sequence

GEO:

Gene Expression Omnibus

GO:

Gene Ontology

IAP:

Inhibitor of apoptosis

IFN:

Interferon

IRF:

Interferon regulatory factor

ISRE:

Interferon-stimulated response element

MAID:

MA plot-based signal intensity-dependent fold-change criterion

MHC:

Major histocompatibility complex

MRI:

Magnetic resonance imaging

MS:

Multiple sclerosis

NAb:

Neutralizing antibodies

NK cells:

Natural killer cells

Pat:

Patient

PBMC:

Peripheral blood mononuclear cells

PPI:

Protein–protein interaction

PWM:

Position weight matrix

RLR:

RIG-I-like receptor

RRMS:

Relapsing–remitting multiple sclerosis

s.c.:

Subcutaneous

SMARC:

SWI/SNF-related matrix-associated actin-dependent regulator of chromatin

TF:

Transcription factor

TFBS:

Transcription factor binding site

TLR:

Toll-like receptor

TSS:

Transcription start site

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Acknowledgments

We thank our laboratory assistants Gabriele Gillwaldt, Ina Schröder, and Ildikó Tóth for their help in performing the experiments. We are also grateful to study nurse Christa Tiffert for her excellent support in conducting this study.

Financial Disclosure

The Affymetrix microarray experiments were partially funded by Merck Serono. The funder had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Conflicts of Interest

U.K. Zettl received research support as well as speaking fees from Bayer, Biogen Idec, Merck Serono, Novartis, Sanofi-Aventis, Almirall, and Teva. M. Hecker received speaking fees from Bayer HealthCare, and Novartis. C. Hartmann, O. Kandulski, B.K. Paap, D. Koczan, and H.-J. Thiesen declare no potential conflicts of interest.

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Authors and Affiliations

  1. Steinbeis Transfer Center for Proteome Analysis, Schillingallee 68, 18057, Rostock, Germany

    Michael Hecker

  2. Division of Neuroimmunology, Department of Neurology, University of Rostock, Gehlsheimer Str. 20, 18147, Rostock, Germany

    Michael Hecker, Christiane Hartmann, Ole Kandulski, Brigitte Katrin Paap & Uwe Klaus Zettl

  3. Institute of Immunology, University of Rostock, Schillingallee 70, 18055, Rostock, Germany

    Dirk Koczan & Hans-Juergen Thiesen

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  1. Michael Hecker
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Correspondence to Michael Hecker.

Electronic Supplementary Material

Below is the link to the electronic supplementary material.

Online Resource 1

Table of the 96 genes that were up- or down-regulated in the PBMC of the MS patients after the start of subcutaneous IFN-beta-1a treatment. In comparison to pre-treatment levels, the genes were found to be expressed at higher or lower transcript levels at at least one time point during therapy. The spreadsheet provides diverse types of information for each gene, e.g. Entrez ID and the official full name, as well as the MAID filtering results, GO term memberships, TFBS and blood cell type specificities. (XLS 115 kb)

Online Resource 2

MX1 mRNA expression levels. The signal intensities for MX1 are presented for all 12 MS patients and all 5 time points from baseline up to two years into treatment. (XLS 28 kb)

Online Resource 3

Short- and long-term changes in blood gene expression during a different IFN-beta therapy. Based on similar temporal expression patterns, six gene clusters were defined using the microarray data of 12 MS patients treated with subcutaneous (sc.) IFN-beta-1a (Fig. 2). Here, we visualized the averaged mRNA dynamics of these genes in the course of therapy with IFN-beta-1b sc. (n = 25 patients). The data were obtained in the same manner as in the present study. However, a different type of microarray was used, and because of that, one of the 96 filtered genes (TREML1, cluster 5) was not contained in this data set. As in Fig. 2, each line shows the mean standardized expression levels (z-scores) of a gene. In general, the gene regulatory effects of the two IFN-beta preparations were very similar. Differences were observed only early in treatment (after 2 days), when the up-regulation of several genes was stronger after IFN-beta-1a sc. administration. (JPEG 621 kb)

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Online Resource 4

Validation of the Affymetrix microarray data by real-time PCR experiments. Real-time PCR was performed for 15 genes that showed significant expression changes in response to IFN-beta therapy in the microarray data. Additionally, GAPDH was measured as a normalization control. The table provides the IDs of the TaqMan assays used, the genes’ transcript levels averaged over the patients as well as the p-values for all time point comparisons. Pearson correlation coefficients and respective p-values were calculated to assess whether the real-time PCR data resemble the microarray data. (XLS 37 kb)

Online Resource 5

Overrepresented terms of the Gene Ontology. P-values were computed for each GO term using the R package GOstats [37]. Functional categories with p-value < 0.001 and to which at least 4 of the 96 filtered genes are associated (“Count”) are shown in the table. “ExpCount” gives the expected number of genes that would belong to the respective term in an arbitrary equally-sized list of genes. (XLS 19 kb)

Online Resource 6

Sub-networks identified in the full gene interaction network (Fig. 4) using the MCODE method. Five disjoint clusters emerged. For instance, on the top right, a cluster is shown that contains IRF1–2 and PRDM1, which are TFs competing for the same regulatory binding sites. On the bottom right, there is a cluster that is mainly built from literature-derived gene interactions linked to chemokines. The network clustering result is available from the authors as a Cytoscape session file upon request. (JPEG 831 kb)

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Hecker, M., Hartmann, C., Kandulski, O. et al. Interferon-beta therapy in multiple sclerosis: the short-term and long-term effects on the patients’ individual gene expression in peripheral blood. Mol Neurobiol 48, 737–756 (2013). https://doi.org/10.1007/s12035-013-8463-1

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