T cell gene expression profiling identifies distinct subgroups of Japanese multiple sclerosis patients

Medicinal Chemistry and Chemical Biology of Nurr1 Modulators: An Emerging Strategy in Neurodegeneration

Nuclear receptor related 1 (Nurr1) is a transcription factor with neuroprotective and antineuroinflammatory properties. Observations from genetic studies and human patients support potential of Nurr1 as a therapeutic target in neurodegeneration, but due to a lack of high-quality chemical tools for pharmacological control of Nurr1, its target validation is pending. Nevertheless, considerable progress has recently been made in elucidating structural and functional characteristics of Nurr1, and several ligand scaffolds have been discovered. Here, we analyze Nurr1's structure and mechanisms compared to other nuclear receptors, summarize the known small molecule Nurr1 ligands, and discuss the available evidence for the therapeutic potential of Nurr1 in neurodegeneration.

The Critical Role of Nurr1 as a Mediator and Therapeutic Target in Alzheimer's Disease-related Pathogenesis

Several studies have revealed that the transcription factor nuclear receptor related 1 (Nurr1) plays several roles not only in the regulation of gene expression related to dopamine synthesis, but also in alternative splicing, and miRNA targeting. Moreover, it regulates cognitive functions and protects against inflammation-induced neuronal death. In particular, the role of Nurr1 in the pathogenesis of Parkinson's disease (PD) has been well investigated; for example, it has been shown that it restores behavioral and histological impairments in PD models. Although many studies have evaluated the connection between Nurr1 and PD pathogenesis, the role of Nurr1 in Alzheimer's disease (AD) remain to be studied. There have been several studies describing Nurr1 protein expression in the AD brain. However, only a few studies have examined the role of Nurr1 in the context of AD. Therefore, in this review, we highlight the overall effects of Nurr1 under the neuropathologic conditions related to AD. Furthermore, we suggest the possibility of using Nurr1 as a therapeutic target for AD or other neurodegenerative disorders.

Role of the Orphan Nuclear Receptor NR4A Family in T-Cell Biology

The nuclear orphan receptors NR4A1, NR4A2, and NR4A3 are immediate early genes that are induced by various signals. They act as transcription factors and their activity is not regulated by ligand binding and are thus regulated via their expression levels. Their expression is transiently induced in T cells by triggering of the T cell receptor following antigen recognition during both thymic differentiation and peripheral T cell responses. In this review, we will discuss how NR4A family members impact different aspects of the life of a T cell from thymic differentiation to peripheral response against infections and cancer.

NURR1 Impairment in Multiple Sclerosis

The transcription factor NURR1 is a constitutively active orphan receptor belonging to the steroid hormone receptor class NR4A. Although a genetic association between NURR1 and autoimmune inflammatory diseases has never emerged from genome-wide association studies (GWAS), alterations in the expression of NURR1 have been observed in various autoimmune diseases. Specifically, its role in autoimmune inflammatory diseases is mainly related to its capability to counteract inflammation. In fact, NURR1 exerts anti-inflammatory functions inhibiting the transcription of the molecules involved in proinflammatory pathways, not only in the peripheral blood compartment, but also in the cerebral parenchyma acting in microglial cells and astrocytes. In parallel, NURR1 has been also linked to dopamine-associated brain disorders, such as Parkinson’s disease (PD) and schizophrenia, since it is involved in the development and in the maintenance of midbrain dopaminergic neurons (mDA). Considering its role in neuro- and systemic inflammatory processes, here we review the evidences supporting its contribution to multiple sclerosis (MS), a chronic inflammatory autoimmune disease affecting the central nervous system (CNS). To date, the specific role of NURR1 in MS is still debated and few authors have studied this topic. Here, we plan to clarify this issue analyzing the reported association between NURR1 and MS in human and murine model studies.

Dynamic Response Genes in CD4+ T Cells Reveal a Network of Interactive Proteins that Classifies Disease Activity in Multiple Sclerosis

Multiple sclerosis (MS) is a chronic inflammatory disease of the CNS and has a varying disease course as well as variable response to treatment. Biomarkers may therefore aid personalized treatment. We tested whether in vitro activation of MS patient-derived CD4+ T cells could reveal potential biomarkers. The dynamic gene expression response to activation was dysregulated in patient-derived CD4+ T cells. By integrating our findings with genome-wide association studies, we constructed a highly connected MS gene module, disclosing cell activation and chemotaxis as central components. Changes in several module genes were associated with differences in protein levels, which were measurable in cerebrospinal fluid and were used to classify patients from control individuals. In addition, these measurements could predict disease activity after 2 years and distinguish low and high responders to treatment in two additional, independent cohorts. While further validation is needed in larger cohorts prior to clinical implementation, we have uncovered a set of potentially promising biomarkers.

Bypassing hazard of housekeeping genes: their evaluation in rat granule neurons treated with cerebrospinal fluid of multiple sclerosis subjects

Gene expression studies employing real-time PCR has become an intrinsic part of biomedical research. Appropriate normalization of target gene transcript(s) based on stably expressed housekeeping genes is crucial in individual experimental conditions to obtain accurate results. In multiple sclerosis (MS), several gene expression studies have been undertaken, however, the suitability of housekeeping genes to express stably in this disease is not yet explored. Recent research suggests that their expression level may vary under different experimental conditions. Hence it is indispensible to evaluate their expression stability to accurately normalize target gene transcripts. The present study aims to evaluate the expression stability of seven housekeeping genes in rat granule neurons treated with cerebrospinal fluid of MS patients. The selected reference genes were quantified by real time PCR and their expression stability was assessed using GeNorm and NormFinder algorithms. GeNorm identified transferrin receptor (Tfrc) and microglobulin beta-2 (B2m) the most stable genes followed by ribosomal protein L19 (Rpl19) whereas β-actin (ActB) and glyceraldehyde-3-phosphate-dehydrogenase (Gapdh) the most fluctuated ones in these neurons. NormFinder identified Tfrc as the best invariable gene followed by B2m and Rpl19. ActB and Gapdh were the least stable genes as analyzed by NormFinder algorithm. Both methods reported Tfrc and B2m the most stably expressed genes and Gapdh the least stable one. Altogether our data demonstrate the significance of pre-validation of housekeeping genes for accurate normalization and indicates Tfrc and B2m as best endogenous controls in MS. ActB and Gapdh are not recommended in gene expression studies related to current one.

Increasing consistency of disease biomarker prediction across datasets

Microarray studies with human subjects often have limited sample sizes which hampers the ability to detect reliable biomarkers associated with disease and motivates the need to aggregate data across studies. However, human gene expression measurements may be influenced by many non-random factors such as genetics, sample preparations, and tissue heterogeneity. These factors can contribute to a lack of agreement among related studies, limiting the utility of their aggregation. We show that it is feasible to carry out an automatic correction of individual datasets to reduce the effect of such ‘latent variables’ (without prior knowledge of the variables) in such a way that datasets addressing the same condition show better agreement once each is corrected. We build our approach on the method of surrogate variable analysis but we demonstrate that the original algorithm is unsuitable for the analysis of human tissue samples that are mixtures of different cell types. We propose a modification to SVA that is crucial to obtaining the improvement in agreement that we observe. We develop our method on a compendium of multiple sclerosis data and verify it on an independent compendium of Parkinson's disease datasets. In both cases, we show that our method is able to improve agreement across varying study designs, platforms, and tissues. This approach has the potential for wide applicability to any field where lack of inter-study agreement has been a concern.

Naive CD4 T-cell activation identifies MS patients having rapid transition to progressive MS

Objective:

Our objective was to determine whether altered naive CD4 T-cell biology contributes to development of disease progression in secondary progressive multiple sclerosis (SPMS).

Methods:

We compared the naive CD4 T-cell gene expression profiles of 19 patients with SPMS and 14 healthy controls (HCs) using a whole-genome microarray approach. We analyzed surface protein expression of critical genes by flow cytometry after T-cell receptor (TCR) stimulation of naive CD4 T cells isolated from HCs and patients with SPMS.

Results:

Hierarchical clustering segregated patients with SPMS into 2 subgroups: SP-1, which had a short duration of relapsing-remitting multiple sclerosis (MS), and SP-2, which had a long duration of relapsing-remitting MS. SP-1 patients upregulated numerous immune genes, including genes within TCR and toll-like receptor (TLR) signaling pathways. SP-2 patients showed immune gene downregulation in comparison with HCs. We identified an SP-1–specific transcriptional signature of 3 genes (TLR4, TLR2, and chemokine receptor 1), and these genes had higher surface protein expression in SP-1 than in SP-2. After TCR stimulation for 48 hours, only SP-1 showed a progressive linear increase in TLR2 and TLR4 protein expression.

Conclusions:

Differences in naive CD4 T-cell biology, notably of TCR and TLR signaling pathways, identified patients with MS with more rapid conversion to secondary progression, a critical determinant of long-term disability in MS.

Transcriptional profiling of multiple sclerosis: towards improved diagnosis and treatment

The development of high-throughput techniques, for example cDNA and oligonucleotide microarrays, for simultaneous analysis of the transcriptional expression of thousands of genes, even the entire genome, has provided new possibilities to get better insights into the pathogenesis of various diseases. This technology has also been applied to define biomarkers and, most importantly, possible new candidate targets for novel treatments. In multiple sclerosis, microarray studies have been performed on brain autopsy and biopsy specimens and peripheral blood. The effects of current treatments for multiple sclerosis, especially interferon-beta and glatiramer acetate, on transcriptional profiles, have also been investigated. We review the main findings revealed from these studies. The emerging potential of microarray technology to define gene signatures, diagnostic and prognostic markers for disease course, and treatment response in multiple sclerosis will be discussed.

Gene expression analysis of relapsing-remitting, primary progressive and secondary progressive multiple sclerosis

BACKGROUND

Previous studies of multiple sclerosis (MS) have indicated differences in the pathogenesis in relapsing-remitting (RRMS), secondary progressive (SPMS) and primary progressive (PPMS) disease.

OBJECTIVE

We hypothesized that different MS subtypes would show differences in gene expression that could be traced to specific subsets of peripheral blood mononuclear cells (PBMCs).

METHODS

Gene expression in RRMS, SPMS, PPMS and healthy control (HC) PBMCs was analyzed on Affymetrix arrays. In addition, we studied gene expression in pools of purified PBMC subsets.

RESULTS

We found 380 genes that were differentially expressed in RRMS, PPMS, SPMS and HCs (false discovery rate < 5%). There were no major differences between the subtypes of MS. The genes showing most prominent expression changes in RRMS were associated with adaptive immune pathways, while genes in PPMS were associated with innate immune system pathways. SPMS patients shared pathways with RRMS and PPMS patients. Gene expression changes were most prominent in B cells, CD8+ T cells and monocytes.

CONCLUSION

Differences in gene expression, which could be traced to B cells, CD8+ T cells and monocytes, were found between MS patients and HCs but only minor differences were observed between MS subgroups.

Genome wide differences of gene expression associated with HLA-DRB1 genotype in multiple sclerosis: a pilot study

Using two microarray platforms, we identify HLA-DRB5 as the most highly expressed gene in MS compared to healthy subjects. As expected, HLA-DRB5 expression was associated with the HLA-DRB1*1501 MS susceptibility allele. Besides HLA-DRB5, there were 1219 differentially expressed exons (p<0.01, |fold change (FC)|>1.2) that differed between HLA-DRB1*1501 Positive multiple sclerosis subjects (MSP) compared to HLA-DRB1*1501 negative multiple sclerosis subjects (MSN). Analysis of the regulated genes revealed significantly different immune signaling pathways including IL-4 and IL-17 in these two MS genotypes. Different risk alleles appear to be associated with different patterns of gene expression that may reflect differences in pathophysiology of these two MS subtypes. These preliminary data will need to be confirmed in future studies.

Resequencing and fine-mapping of the chromosome 12q13-14 locus associated with multiple sclerosis refines the number of implicated genes

Multiple sclerosis (MS) is a common chronic inflammatory disease of the central nervous system. Susceptibility to the disease is affected by both environmental and genetic factors. Genetic factors include haplotypes in the histocompatibility complex (MHC) and over 50 non-MHC loci reported by genome-wide association studies. Amongst these, we previously reported polymorphisms in chromosome 12q13-14 with a protective effect in individuals of European descent. This locus spans 288 kb and contains 17 genes, including several candidate genes which have potentially significant pathogenic and therapeutic implications. In this study, we aimed to fine-map this locus. We have implemented a two-phase study: a variant discovery phase where we have used next-generation sequencing and two target-enrichment strategies [long-range polymerase chain reaction (PCR) and Nimblegen's solution phase hybridization capture] in pools of 25 samples; and a genotyping phase where we genotyped 712 variants in 3577 healthy controls and 3269 MS patients. This study confirmed the association (rs2069502, P = 9.9 × 10(-11), OR = 0.787) and narrowed down the locus of association to an 86.5 kb region. Although the study was unable to pinpoint the key-associated variant, we have identified a 42 (genotyped and imputed) single-nucleotide polymorphism haplotype block likely to harbour the causal variant. No evidence of association at previously reported low-frequency variants in CYP27B1 was observed. As part of the study we compared variant discovery performance using two target-enrichment strategies. We concluded that our pools enriched with Nimblegen's solution phase hybridization capture had better sensitivity to detect true variants than the pools enriched with long-range PCR, whilst specificity was better in the long-range PCR-enriched pools compared with solution phase hybridization capture enriched pools; this result has important implications for the design of future fine-mapping studies.

Beta-lactam antibiotics modulate T-cell functions and gene expression via covalent binding to cellular albumin

Recent work has suggested that beta-lactam antibiotics might directly affect eukaryotic cellular functions. Here, we studied the effects of commonly used beta-lactam antibiotics on rodent and human T cells in vitro and in vivo on T-cell-mediated experimental autoimmune diseases. We now report that experimental autoimmune encephalomyelitis and adjuvant arthritis were significantly more severe in rats treated with cefuroxime and other beta-lactams. T cells appeared to mediate the effect: an anti-myelin basic protein T-cell line treated with cefuroxime or penicillin was more encephalitogenic in adoptive transfer experiments. The beta-lactam ampicillin, in contrast to cefuroxime and penicillin, did not enhance encephalomyelitis, but did inhibit the autoimmune diabetes developing spontaneously in nonobese diabetic mice. Gene expression analysis of human peripheral blood T cells showed that numerous genes associated with T helper 2 (Th2) and T regulatory (Treg) differentiation were down-regulated in T cells stimulated in the presence of cefuroxime; these genes were up-regulated in the presence of ampicillin. The T-cell protein that covalently bound beta-lactam antibiotics was found to be albumin. Human and rodent T cells expressed albumin mRNA and protein, and penicillin-modified albumin was taken up by rat T cells, leading to enhanced encephalitogenicity. Thus, beta-lactam antibiotics in wide clinical use have marked effects on T-cell behavior; beta-lactam antibiotics can function as immunomodulators, apparently through covalent binding to albumin.

Clinical and magnetic resonance imaging features of multiple sclerosis with autoreactive antibodies in Ishikawa Prefecture, Japan

Previous reports of multiple sclerosis (MS) with autoantibodies might include neuromyelitis optica (NMO). We investigated the frequency of autoreactive antibodies (AR) in both MS and NMO. Systemic lupus erythematosus (SLE)-associated autoantibodies such as anti-Sm antibodies, anti-single stranded DNA antibodies and lupus anticoagulants were only identified in MS, whereas SLE itself is more commonly associated with NMO. Moreover, when magnetic resonance imaging features between autoreactive antibody-positive (AR(+))MS and -negative (AR(-))MS were compared, AR(+)MS cases showed significantly fewer than 3 periventricular lesions compared to AR(-)MS cases. These results may indicate different pathogenetic mechanisms underlying AR(+)MS and AR(-)MS.

MS risk genes are transcriptionally regulated in CSF leukocytes at relapse

Background:

Infiltrating T-helper cells, cytotoxic T-cells, B-cells and monocytes are thought to mediate the damage to myelin, oligodendrocytes and axons in multiple sclerosis (MS), which results in progressive disability.

Objective:

The objective of this paper is to explore gene expression profiles of leukocytes in the cerebrospinal fluid (CSF) compartment of MS patients during relapse.

Methods:

Global gene expression was analyzed by DNA microarray analysis of cells in CSF from MS patients and controls, and verifications were performed with real-time polymerase chain reaction (PCR) and enzyme-linked immunosorbent assay (ELISA).

Results:

Fifty percent of the recently described risk genes for MS and 28% of non-risk genes were differently expressed in MS patients compared to controls (χ2-test, p=7.7 × 10−5). Genes involved in T- and NK-cell processes were up-regulated, and genes involved in processes targeting innate immunity or B-cells were down-regulated in MS. Increased expression of EDN1 and CXCL11 and decreased expression of HMOX1 was verified with real-time PCR and increased expression of CXCL13 was verified with ELISA in CSF.

Conclusion:

DNA microarray analysis is useful in identifying differently expressed genes in CSF leukocytes, which may be important in MS in vivo. Our findings suggest that many of the risk genes for MS are differently expressed in the disease-mediating leukocytes that penetrate the blood-brain barrier.

Multiple sclerosis-linked and interferon-beta-regulated gene expression in plasmacytoid dendritic cells

The cause of multiple sclerosis (MS) is not known and the mechanism of interferon-beta, a disease-modifying treatment, is not well-understood. We studied gene expression in plasmacytoid dendritic cells (pDCs), antigen-presenting cells implicated in MS pathogenesis. PDCs were separated from healthy donors and MS patients at two time points: before and after initiation of treatment with interferon-beta. Expression of selected MS-linked and interferon-beta-regulated genes was validated with single assays. We have identified 60 genes which were abnormally expressed in MS patients and were corrected after treatment. These genes could be studied as potential MS biomarkers and possible therapeutic targets in MS.

Reassessment of blood gene expression markers for the prognosis of relapsing-remitting multiple sclerosis

Despite considerable advances in the treatment of multiple sclerosis, current drugs are only partially effective. Most patients show reduced disease activity with therapy, but still experience relapses, increasing disability, and new brain lesions. Since there are no reliable clinical or biological markers of disease progression, long-term prognosis is difficult to predict for individual patients. We identified 18 studies that suggested genes expressed in blood as predictive biomarkers. We validated the prognostic value of those genes with three different microarray data sets comprising 148 patients in total. Using these data, we tested whether the genes were significantly differentially expressed between patients with good and poor courses of the disease. Poor progression was defined by relapses and/or increase of disability during a two-year follow-up, independent of the administered therapy. Of 110 genes that have been proposed as predictive biomarkers, most could not be confirmed in our analysis. However, the G protein-coupled membrane receptor GPR3 was expressed at significantly lower levels in patients with poor disease progression in all data sets. GPR3 has therefore a high potential to be a biomarker for predicting future disease activity. In addition, we examined the IL17 cytokines and receptors in more detail and propose IL17RC as a new, promising, transcript-based biomarker candidate. Further studies are needed to better understand the roles of these receptors in multiple sclerosis and its treatment and to clarify the utility of GPR3 and IL17RC expression levels in the blood as markers of long-term prognosis.

Sieving treatment biomarkers from blood gene-expression profiles: a pharmacogenomic update on two types of multiple sclerosis therapy

Interferon-β (IFN-β) and glatiramer acetate are routinely used to inhibit disease activity in multiple sclerosis, but their mechanisms of action are incompletely understood. Individual treatment responses vary and candidate molecular markers that predict them have yet to be established. Why some patients respond poorly to a certain treatment while others respond well is addressed by the pharmacogenomic approach, which postulates that the molecular response to treatment correlates with the clinical effects, and thus seeks biological markers to estimate prognosis, guide therapy, comprehend the drugs' mechanisms of action and offer insights into disease pathogenesis. A poor clinical response can be owing to genetic variants in drug receptors or signaling components, or the appearance of neutralizing antibodies that interfere with the drug's binding efficacy. Independently, such mechanisms could lead to inadequate, that is to say unchanged, molecular responses, or exceedingly increased or decreased changes. By means of DNA microarray studies, various research groups endeavour to establish a clinically relevant relationship between the biological response to these drugs and treatment effects. Molecular profiles obtained in this way differ in the pattern and number of modulated genes, suggesting the existence of an individual 'drug-response fingerprint'. To further unravel the underlying regulatory interaction structure of the genes responsive to these immunotherapies represents a daunting but inevitable task. In this article, we focus on longitudinal ex vivo transcriptomic studies in multiple sclerosis and its therapy. We will discuss recurrently reported biomarker candidates, emphasizing those of immunologically meaning, and review studies with network module outputs.

Systematic review of genome-wide expression studies in multiple sclerosis

Background Although recent genome-wide association studies have identified several genetic variants contributing to the complex aetiology of multiple sclerosis (MS), expression and functional studies are required to further understand its molecular basis. Objectives To identify genes and pathways with differential expression in MS. Design The authors conducted a systematic review of seven microarray studies, in which expression in immune cells was compared between MS patients and controls. These studies include a previously unpublished study, which is described here in detail. Results and conclusion Although in general the overlap between studies was poor, 229 genes were found to be differentially expressed in MS in at least two studies, of which 11 were in three studies and HSPA1A in four studies. After excluding the authors' unpublished experiment which may have been affected by certain confounding factors and inclusion of treated subjects, 135 genes were identified in at least two studies. The differentially expressed genes were significantly associated with several immunological pathways, including interleukin (IL)-4, IL-6, IL-17 and glucocorticoid receptor signalling pathways. 15 of the 229 loci have shown some association with MS in published genome-wide association studies (p<0.0001), including three loci with confirmed MS risk variants.

Gene expression studies in multiple sclerosis

Multiple sclerosis (MS) is a serious neurological disorder affecting young Caucasian individuals, usually with an age of onset at 18 to 40 years old. Females account for approximately 60x of MS cases and the manifestation and course of the disease is highly variable from patient to patient. The disorder is characterised by the development of plaques within the central nervous system (CNS). Many gene expression studies have been undertaken to look at the specific patterns of gene transcript levels in MS. Human tissues and experimental mice were used in these gene-profiling studies and a very valuable and interesting set of data has resulted from these various expression studies. In general, genes showing variable expression include mainly immunological and inflammatory genes, stress and antioxidant genes, as well as metabolic and central nervous system markers. Of particular interest are a number of genes localised to susceptible loci previously shown to be in linkage with MS. However due to the clinical complexity of the disease, the heterogeneity of the tissues used in expression studies, as well as the variable DNA chips/membranes used for the gene profiling, it is difficult to interpret the available information. Although this information is essential for the understanding of the pathogenesis of MS, it is difficult to decipher and define the gene pathways involved in the disorder. Experiments in gene expression profiling in MS have been numerous and lists of candidates are now available for analysis. Researchers have investigated gene expression in peripheral mononuclear white blood cells (PBMCs), in MS animal models Experimental Allergic Encephalomyelitis (EAE) and post mortem MS brain tissues. This review will focus on the results of these studies.

Gene expression profiling for discovery of novel targets in human traumatic brain injury

Several clinical trials have failed to demonstrate a significant effect on outcome following human traumatic brain injury (TBI) despite promising results obtained in preclinical animal studies. These failures may be due in part to a misinterpretation of the findings obtained in preclinical animal models of TBI, a misunderstanding of the complexity of the human response to TBI, limited knowledge about the biological pathways that interact to contribute to good and bad outcomes after brain injury, and the effects of genomic variability and environment on individual recovery. Recent publications suggest that data obtained from gene expression profiling studies of complex neurological diseases such as stroke, multiple sclerosis (MS), Alzheimer's and Parkinson's may contribute to a more informed understanding of what affects outcome following TBI. These data may help to bridge the gap between successful preclinical studies and negative clinical trials in humans to reveal novel targets for therapy. Gene expression profiling has the capability to identify biomarkers associated with response to TBI, elucidate complex genetic interactions that may play a role in outcome following TBI, and reveal biological pathways related to brain health. This review highlights the current state of the literature on gene expression profiling for neurological disease and discusses its ability to aid in unraveling the variable human response to TBI and the potential for it to offer treatment strategies in an area where we currently have limited therapeutic options primarily based on supportive care.

The multiple sclerosis whole blood mRNA transcriptome and genetic associations indicate dysregulation of specific T cell pathways in pathogenesis

Multiple sclerosis (MS) is an autoimmune disease with a genetic component, caused at least in part by aberrant lymphocyte activity. The whole blood mRNA transcriptome was measured for 99 untreated MS patients: 43 primary progressive MS, 20 secondary progressive MS, 36 relapsing remitting MS and 45 age-matched healthy controls. The ANZgene Multiple Sclerosis Genetics Consortium genotyped more than 300 000 SNPs for 115 of these samples. Transcription from genes on translational regulation, oxidative phosphorylation, immune synapse and antigen presentation pathways was markedly increased in all forms of MS. Expression of genes tagging T cells was also upregulated (P < 10(-12)) in MS. A T cell gene signature predicts disease state with a concordance index of 0.79 with age and gender as co-variables, but the signature is not associated with clinical course or disability. The ANZgene genome wide association screen identified two novel regions with genome wide significance: one encoding the T cell co-stimulatory molecule, CD40; the other a region on chromosome 12q13-14. The CD40 haplotype associated with increased MS susceptibility has decreased gene expression in MS (P < 0.0007). The second MS susceptibility region includes 17 genes on 12q13-14 in tight linkage disequilibrium. Of these, only 13 are expressed in leukocytes, and of these the expression of one, FAM119B, is much lower in the susceptibility haplotype (P < 10(-14)). Overall, these data indicate dysregulation of T cells can be detected in the whole blood of untreated MS patients, and supports targeting of activated T cells in therapy for all forms of MS.

Long-term genome-wide blood RNA expression profiles yield novel molecular response candidates for IFN-β-1b treatment in relapsing remitting MS

Aims: In multiple sclerosis patients, treatment with recombinant IFN-β (rIFN-β) is partially efficient in reducing clinical exacerbations. However, its molecular mechanism of action is still under scrutiny. Materials & methods: We used DNA microarrays (Affymetrix, CA, USA) and peripheral mononuclear blood cells from 25 relapsing remitting multiple sclerosis patients to analyze the longitudinal transcriptional profile within 2 years of rIFN-β administration. Sets of differentially expressed genes were attained by applying a combination of independent criteria, thereby providing efficient data curation and gene filtering that accounted for technical and biological noise. Gene ontology term-association analysis and scientific literature text mining were used to explore evidence of gene interaction. Results: Post-therapy initiation, we identified 42 (day 2), 175 (month 1), 103 (month 12) and 108 (month 24) differentially expressed genes. Increased expression of established IFN-β marker genes, as well as differential expression of circulating IFN-β-responsive candidate genes, were observed. MS4A1 (CD20), a known target of B-cell depletion therapy, was significantly downregulated after one month. CMPK2, FCER1A, and FFAR2 appeared as hitherto unrecognized multiple sclerosis treatment-related differentially expressed genes that were consistently modulated over time. Overall, 84 interactions between 54 genes were attained, of which two major gene networks were identified at an earlier stage of therapy: the first (n = 15 genes) consisted of mostly known IFN-β-activated genes, whereas the second (n = 12) mainly contained downregulated genes that to date have not been associated with IFN-β effects in multiple sclerosis array research. Conclusion: We achieved both a broadening of the knowledge of IFN-β mechanism-of-action-related constituents and the identification of time-dependent interactions between IFN-β regulated genes.

Learning from nature: pregnancy changes the expression of inflammation-related genes in patients with multiple sclerosis

Background

Pregnancy is associated with reduced activity of multiple sclerosis (MS). However, the biological mechanisms underlying this pregnancy-related decrease in disease activity are poorly understood.

Methodology

We conducted a genome-wide transcription analysis in peripheral blood mononuclear cells (PBMCs) from 12 women (7 MS patients and 5 healthy controls) followed during their pregnancy. Samples were obtained before, during (i.e. at the third, sixth, and ninth month of gestation) and after pregnancy. A validation of the expression profiles has been conducted by using the same samples and an independent group of 25 MS patients and 11 healthy controls. Finally, considering the total group of 32 MS patients, we compared expression profiles of patients relapsing during pregnancy (n = 6) with those of relapse-free patients (n = 26).

Principal Findings

Results showed an altered expression of 347 transcripts in non-pregnant MS patients with respect to non-pregnant healthy controls. Complementary changes in expression, occurring during pregnancy, reverted the previous imbalance particularly for seven inflammation-related transcripts, i.e. SOCS2, TNFAIP3, NR4A2, CXCR4, POLR2J, FAM49B, and STAG3L1. Longitudinal analysis showed that the overall deregulation of gene expression reverted to “normal” already within the third month of gestation, while in the post-partum gene expressions rebounded to pre-pregnancy levels. Six (18.7%) of the 32 MS patients had a relapse during pregnancy, mostly in the first trimester. The latter showed delayed expression profiles when compared to relapse-free patients: in these patients expression imbalance was reverted later in the pregnancy, i.e. at sixth month.

Conclusions

Specific changes in expression during pregnancy were associated with a decrease in disease activity assessed by occurrence of relapses during pregnancy. Findings might help in understanding the pathogenesis of MS and may provide basis for the development of novel therapeutic strategies.

Gene expression profiling in multiple sclerosis: a disease of the central nervous system, but with relapses triggered in the periphery?

The aetiology of multiple sclerosis (MS), an autoimmune demyelinating disease of the central nervous system (CNS), includes both genetic and environmental factors, but the pathogenesis is still incompletely known. We performed gene expression profiling on paired cerebrospinal fluid (CSF) and peripheral blood mononuclear cells (PBMCs) samples from 26 MS patients without immunomodulatory treatment, sampled in relapse or remission, and 18 controls using Human Genome U133 plus 2.0 arrays (Affymetrix). In the CSF, 939 probe sets detected differential expression in MS patients compared to controls, but none in PBMCs, confirming that CSF cells might mirror the disease processes. The regulation of selected transcripts in CSF of MS patients was confirmed by quantitative PCR. Unexpectedly however, when comparing MS patients in relapse to those in remission, 266 probe sets detected differential expression in PBMCs, but not in CSF cells, indicating the importance of events outside of the CNS in the triggering of relapse.

Constrained mixture estimation for analysis and robust classification of clinical time series

Motivation: Personalized medicine based on molecular aspects of diseases, such as gene expression profiling, has become increasingly popular. However, one faces multiple challenges when analyzing clinical gene expression data; most of the well-known theoretical issues such as high dimension of feature spaces versus few examples, noise and missing data apply. Special care is needed when designing classification procedures that support personalized diagnosis and choice of treatment. Here, we particularly focus on classification of interferon-β (IFNβ) treatment response in Multiple Sclerosis (MS) patients which has attracted substantial attention in the recent past. Half of the patients remain unaffected by IFNβ treatment, which is still the standard. For them the treatment should be timely ceased to mitigate the side effects.

Results: We propose constrained estimation of mixtures of hidden Markov models as a methodology to classify patient response to IFNβ treatment. The advantages of our approach are that it takes the temporal nature of the data into account and its robustness with respect to noise, missing data and mislabeled samples. Moreover, mixture estimation enables to explore the presence of response sub-groups of patients on the transcriptional level. We clearly outperformed all prior approaches in terms of prediction accuracy, raising it, for the first time, >90%. Additionally, we were able to identify potentially mislabeled samples and to sub-divide the good responders into two sub-groups that exhibited different transcriptional response programs. This is supported by recent findings on MS pathology and therefore may raise interesting clinical follow-up questions.

Availability: The method is implemented in the GQL framework and is available at http://www.ghmm.org/gql. Datasets are available at http://www.cin.ufpe.br/∼igcf/MSConst

Contact:igcf@cin.ufpe.br

Supplementary information:Supplementary data are available at Bioinformatics online.

Systems-based medicine approaches to understand and treat complex diseases. The example of multiple sclerosis

Systems medicine is an emerging concept that acknowledges the complexity of a multitude of non-linear interactions among molecular and physiological variables. Under this new paradigm, rather than a collection of symptoms, diseases are seen as the product of deviations from a robust steady state compatible with life. This concept requires the incorporation of mathematics and physics to the more classical arsenal of physiology and molecular biology with which physicians are trained today. This review explores the diverse types of information that can be accumulated towards the understanding of multiple sclerosis (MS), a complex autoimmune disease that targets the central nervous system (CNS). The challenge of data integration and modeling of dynamical systems is discussed in the context of disease susceptibility and response to treatment. A theoretical framework that supports the use of combination therapy is also presented.

Genome-wide association study identifies new multiple sclerosis susceptibility loci on chromosomes 12 and 20

To identify multiple sclerosis (MS) susceptibility loci, we conducted a genome-wide association study (GWAS) in 1,618 cases and used shared data for 3,413 controls. We performed replication in an independent set of 2,256 cases and 2,310 controls, for a total of 3,874 cases and 5,723 controls. We identified risk-associated SNPs on chromosome 12q13-14 (rs703842, P = 5.4 x 10(-11); rs10876994, P = 2.7 x 10(-10); rs12368653, P = 1.0 x 10(-7)) and upstream of CD40 on chromosome 20q13 (rs6074022, P = 1.3 x 10(-7); rs1569723, P = 2.9 x 10(-7)). Both loci are also associated with other autoimmune diseases. We also replicated several known MS associations (HLA-DR15, P = 7.0 x 10(-184); CD58, P = 9.6 x 10(-8); EVI5-RPL5, P = 2.5 x 10(-6); IL2RA, P = 7.4 x 10(-6); CLEC16A, P = 1.1 x 10(-4); IL7R, P = 1.3 x 10(-3); TYK2, P = 3.5 x 10(-3)) and observed a statistical interaction between SNPs in EVI5-RPL5 and HLA-DR15 (P = 0.001).

Monitoring of multiple sclerosis immunotherapy: from single candidates to biomarker networks

Applying microarray technology to identify new diagnostic and prognostic markers in peripheral blood cells (PBC) after therapeutic intervention opens great perspectives regarding patient subclassification. Three recombinant products of the pleiotropic agent interferon beta (rIFN-beta) are available for disease modifying therapy of relapsing remitting multiple sclerosis (RRMS), a complex inflammatory autoimmune disease that targets the central nervous system. They differ according to formulation, route of administration and dosage regimens. The currently, only partially understood mechanism of action of injected rIFN-beta into human organisms needs provision with accessory key molecules; in addition, the significance of established clinical IFN-beta response criteria that distinguish responding from non-responding patients remain unclear.With respect to these major questions, we discuss promising candidates on the gene transcription level, attained from scientific MS literature that included a longitudinal aspect. Reviewed studies were in part carried out with distinct gene interrogating platforms (GeneArrays; RT-PCR), settings (in vitro; ex vivo), and study designs (drug formulations and regimen; inclusion criteria and clinical endpoints), hampering meaningful meta-analysis. Nevertheless, PBC from therapy-naïve MS patients, rIFN-beta treated MS patients, and healthy controls served to characterize facets of both the disease and its treatment. Hence, the field of MS transcriptomics in immunomodulatory therapy is (by far) not adequately understood and should be embedded into systems biology disciplines, yielding multi-layer analyses that deliver timely identification of MS subjects who will profit from applied rIFN-beta therapy.

[Recent progress in bioinformatics for microarray analysis]

Following the completion of the Human Genome Project in 2003, we were able to clarify the comprehensive profile of the whole human genome on DNA microarray. KeyMolnet is a bioinformatics tool for analyzing molecular interactions on the curated knowledge database. It promotes genome-based drug discovery research aimed at mining the most relevant molecular target to personalized medicine. Multiple sclerosis (MS) is an inflammatory demyelinating disease with a relapsing-remitting clinical course, affecting exclusively the human central nervous system white matter. By DNA microarray, we identified a set of differentially expressed genes in T lymphocytes between MS and healthy subjects, and between acute relapse and complete remission. Hierarchical clustering of discriminator genes established the molecular classification of MS subgroups, associated with the therapeutic response to interferon-beta. The molecular network of the genes involved in development of MS and induction of acute relapse of MS was related to NF-kappaB-regulated gene expression. Prion diseases are an intractable neurodegenerative disease, mediated by an abnormal prion protein PrPSc. The protein conformational conversion from the cellular prion protein PrPC to PrPSc requires an as yet unidentified species-specific chaperone named "Protein X". By protein microarray, we identified a set of novel PrPC interactors, serving as the candidate for X. The molecular network of PrPC and interactors was closely associated with signaling pathways essential for cell survival, differentiation, proliferation and apoptosis. Thus, the molecular network analysis is highly valuable to extract biological implications from a huge amount of microarray data.

Genomic effects of once-weekly, intramuscular interferon-beta1a treatment after the first dose and on chronic dosing: Relationships to 5-year clinical outcomes in multiple sclerosis patients

PURPOSE

To characterize gene expression in multiple sclerosis (MS) patients after the first dose and chronic dosing of 30 microg, once weekly, intramuscular interferon-beta1a (IFN-beta) and to delineate the pharmacogenomic differences between Good Responders and Partial Responders to IFN-beta therapy.

METHODS

The treatment responses after the first IFN-beta dose and chronic IFN-beta dosing were assessed in 22 relapsing MS patients (17 females, 5 males; average age: 41.5+/-SD 10.4 years). Gene expression profiles in peripheral blood mononuclear cells were obtained prior to treatment and at 1, 2, 4, 8, 24, 48, 120, 168 h after the first IFN-beta dose and at 1, 6 and 12 months after chronic dosing with once-weekly 30 microg IFN-beta-1a intramuscularly. Repeated measures statistics with false discovery rate control were used. The functional characteristics, biological pathways and transcription factor sites were analyzed.

RESULTS

Of the 1000 genes modulated following the first dose and upon chronic dosing of IFN-beta in MS patients, approximately 35% were up-regulated and 65% were down- regulated; the percentage of modulated genes in common was approximately 50%. The expression of the pharmacodynamic mRNA markers of IFN-beta effect showed differences in time profiles for the Good Responder and Partial Responders to IFN-beta therapy and the Jak-STAT, TNFRSF10B, IL6, TGFbeta, retinoic acid and CDC42 pathways were differentially modulated. The patients with side effects to therapy showed differences in the TGFbeta1, IFNG/STAT3 and TNF pathways.

CONCLUSIONS

Gene expression is a valuable tool for understanding the molecular mechanisms of IFN-beta action in MS patients.

Abrogation of T cell quiescence characterizes patients at high risk for multiple sclerosis after the initial neurological event

Clinically isolated syndrome (CIS) refers to the earliest clinical manifestation of multiple sclerosis (MS). Currently there are no prognostic biological markers that accurately predict conversion of CIS to clinically definite MS (CDMS). Furthermore, the earliest molecular events in MS are still unknown. We used microarrays to study gene expression in naïve CD4(+) T cells from 37 CIS patients at time of diagnosis and after 1 year. Supervised machine-learning methods were used to build predictive models of disease conversion. We identified 975 genes whose expression segregated CIS patients into four distinct subgroups. A subset of 108 genes further discriminated patients in one of these (group 1) from other CIS patients. Remarkably, 92% of patients in group 1 converted to CDMS within 9 months. Consistent down-regulation of TOB1, a critical regulator of cell proliferation, was characteristic of group 1 patients. Decreased TOB1 expression at the RNA and protein levels also was confirmed in experimental autoimmune encephalomyelitis. Finally, a genetic association was observed between TOB1 variation and MS progression in an independent cohort. These results indicate that CIS patients at high risk of conversion have impaired regulation of T cell quiescence, possibly resulting in earlier activation of pathogenic CD4(+) cells.

Orphan nuclear receptor NR4A2 expressed in T cells from multiple sclerosis mediates production of inflammatory cytokines

Multiple sclerosis (MS) is an autoimmune disease of the central nervous system (CNS) mediated by Th17 and Th1 cells. DNA microarray analysis previously showed that NR4A2, an orphan nuclear receptor, is strongly up-regulated in the peripheral blood T cells of MS. Here, we report that NR4A2 plays a pivotal role for mediating cytokine production from pathogenic T cells. In experimental autoimmune encephalomyelitis (EAE), an animal model of MS, NR4A2, was selectively up-regulated in the T cells isolated from the CNS. Strikingly, a forced expression of NR4A2 augmented promoter activities of IL-17 and IFN-gamma genes, leading to an excessive production of these cytokines. Conversely, treatment with siRNA for NR4A2, resulted in a significant reduction in the production of IL-17 and IFN-gamma. Furthermore, treatment with NR4A2 siRNA reduced the ability of encephalitogenic T cells to transfer EAE in recipient mice. Thus, NR4A2 is an essential transcription factor for triggering the inflammatory cascade of MS/EAE and may serve as a therapeutic target.

Neuromyelitis optica/Devic's disease: gene expression profiling of brain lesions

Neuromyelitis optica (NMO), also known as Devic's disease, is an inflammatory demyelinating disease that affects selectively the optic nerves and the spinal cord, possibly mediated by an immune mechanism distinct from that of multiple sclerosis (MS). Recent studies indicate that NMO also involves the brain. Here, we studied gene expression profile of brain lesions of a patient with NMO by using DNA microarray, along with gene expression profile of the brains of Parkinson disease and amyotrophic lateral sclerosis patients. We identified more than 200 genes up-regulated in NMO brain lesions. The top 20 genes were composed of the molecules closely associated with immune regulation, among which marked up-regulation of interferon gamma-inducible protein 30 (IFI30), CD163, and secreted phosphoprotein 1 (SPP1, osteopontin) was validated by real time RT-PCR, Northern blot and Western blot analysis. Pathologically, CD68(+) macrophages and microglia expressed intense immunoreactivities for IFI30 and CD163 in NMO lesions, consisting of inflammatory demyelination, axonal loss, necrosis, cavity formation, and vascular fibrosis. KeyMolnet, a bioinformatics tool for analyzing molecular interaction on the curated knowledge database, suggested that the molecular network of up-regulated genes in NMO brain lesions involves transcriptional regulation by the nuclear factor-kappaB (NF-kappaB) and B-lymphocyte-induced maturation protein-1 (Blimp-1). These results suggest that profound activation of the macrophage-mediated proinflammatory immune mechanism plays a pivotal role in development of NMO brain lesions.

Multiple sclerosis genetics

Multiple sclerosis (MS) clusters with the so-called complex genetic diseases, a group of common disorders characterized by modest disease risk heritability and multifaceted gene-environment interactions. The major histocompatibility complex (MHC) is the only genomic region consistently associated with MS, and susceptible MHC haplotypes have been identified. Although the MHC does not account for all genetic contribution to MS, the other genetic contributors have been elusive. Microarray gene-expression studies, which also have not identified a major MS locus, have, however, been promising in elucidating some of the possible pathways involved in the disease. Yet, microarray studies thus far have been unable to separate the genetic causes of MS from the expression consequences of MS. The use of new methodologies and technologies to refine the phenotype, such as brain spectroscopy, PET and functional magnetic resonance imaging combined with novel computational tools and a better understanding of the human genome architecture, may help resolve the genetic causes of MS.

Aberrant transcriptional regulatory network in T cells of multiple sclerosis

To identify the molecular network of the genes deregulated in multiple sclerosis (MS), we studied gene expression profile of purified CD3(+) T cells isolated from Hungarian monozygotic MS twins by DNA microarray analysis. By comparing three concordant and one discordant pairs, we identified 20 differentially expressed genes (DEG) between the MS patient and the genetically identical healthy subject. Molecular network of 20 DEG analyzed by KeyMolnet, a comprehensive information platform, indicated the close relationship with transcriptional regulation by the Ets transcription factor family and the nuclear factor NF-kappaB. This novel bioinformatic approach proposes the logical hypothesis that aberrant regulation of the complex transcriptional regulatory network contributes to development of pathogenic T cells in MS.

Genome-scale Assessment of Molecular Pathology in Systemic Autoimmune Diseases using Microarray Technology: A Potential Breakthrough Diagnostic and Individualized Therapy-design Tool

Systemic autoimmune rheumatic diseases are of complex aetiology, characterized by an intricate interplay of various factors. A myriad of genes lies behind the heterogeneous manifestations of these diseases, and the overexpression and repression of particular genes form a specific gene-expression profile (genetic fingerprints) that is characteristic to the given disease phenotype. Besides the description of various cell types by using gene-expression profiling, the data should be directly applicable to the design of individual therapeutic protocols for patients suffering from various autoimmune diseases. In this review, we summarize the gene-expression profile, various genetic signatures of different autoimmune diseases and give an overview on the possible interpretations of the data. The application of recent breakthroughs in high-throughput molecular profiling technologies, such as microarray technology has been the basis for a revolution in biomedical research, as well as diagnostics and pharmaceutical development. It is easy to envision a day when personalized medicine, which is the diagnosis and treatment of a given patient with agents and procedures tailored to that patient's genetics, physiology and pathology, will become the standard of care.

Microarray analysis identifies a set of CXCR3 and CCR2 ligand chemokines as early IFNbeta-responsive genes in peripheral blood lymphocytes in vitro: an implication for IFNbeta-related adverse effects in multiple sclerosis

Background

A substantial proportion of multiple sclerosis (MS) patients discontinue interferon-beta (IFNβ) treatment due to various adverse effects, most of which emerge at the early phase after initiation of the treatment and then diminish with time. At present, the molecular mechanism underlying IFNβ-related adverse effects remains largely unknown. The aim of this study is to identify a comprehensive list of early IFNβ-responsive genes (IRGs) in peripheral blood mononuclear cells (PBMC) that may play a key role in induction of adverse effects.

Methods

Total RNA of PBMC exposed to 50 ng/ml recombinant human IFNβ for 3 to 24 hours in vitro was processed for cDNA microarray analysis, followed by quantitative real-time RT-PCR analysis.

Results

Among 1,258 genes on the array, IFNβ elevated the expression of 107 and 87 genes, while it reduced the expression of 22 and 23 genes at 3 and 24 hours, respectively. Upregulated IRGs were categorized into conventional IFN-response markers, components of IFN-signaling pathways, chemokines, cytokines, growth factors, and their receptors, regulators of apoptosis, DNA damage, and cell cycle, heat shock proteins, and costimulatory and adhesion molecules. IFNβ markedly upregulated CXCR3 ligand chemokines (SCYB11, SCYB10 and SCYB9) chiefly active on effector T helper type 1 (Th1) T cells, and CCR2 ligand chemokines (SCYA8 and SCYA2) effective on monocytes, whereas it downregulated CXCR2 ligand chemokines (SCYB2, SCYB1 and IL8) primarily active on neutrophils.

Conclusion

IFNβ immediately induces a burst of gene expression of proinflammatory chemokines in vitro that have potential relevance to IFNβ-related early adverse effects in MS patients in vivo.