Systems biology approaches for the study of multiple sclerosis

Serum Neuroinflammatory Disease-Induced Central Nervous System Proteins Predict Clinical Onset of Experimental Autoimmune Encephalomyelitis

There is an urgent need in multiple sclerosis (MS) patients to develop biomarkers and laboratory tests to improve early diagnosis, predict clinical relapses, and optimize treatment responses. In healthy individuals, the transport of proteins across the blood–brain barrier (BBB) is tightly regulated, whereas, in MS, central nervous system (CNS) inflammation results in damage to neuronal tissues, disruption of BBB integrity, and potential release of neuroinflammatory disease-induced CNS proteins (NDICPs) into CSF and serum. Therefore, changes in serum NDICP abundance could serve as biomarkers of MS. Here, we sought to determine if changes in serum NDICPs are detectable prior to clinical onset of experimental autoimmune encephalomyelitis (EAE) and, therefore, enable prediction of disease onset. Importantly, we show in longitudinal serum specimens from individual mice with EAE that pre-onset expression waves of synapsin-2, glutamine synthetase, enolase-2, and synaptotagmin-1 enable the prediction of clinical disease with high sensitivity and specificity. Moreover, we observed differences in serum NDICPs between active and passive immunization in EAE, suggesting hitherto not appreciated differences for disease induction mechanisms. Our studies provide the first evidence for enabling the prediction of clinical disease using serum NDICPs. The results provide proof-of-concept for the development of high-confidence serum NDICP expression waves and protein biomarker candidates for MS.

Body fluid biomarkers in multiple sclerosis: how far we have come and how they could affect the clinic now and in the future

Multiple sclerosis (MS) is an autoimmune inflammatory disease of the central nervous system, which affects over 2.5 million people worldwide. Although MS has been extensively studied, many challenges still remain in regards to treatment, diagnosis and prognosis. Typically, prognosis and individual responses to treatment are evaluated by clinical tests such as the expanded disability status scale, MRI and presence of oligoclonal bands in the cerebrospinal fluid. However, none of these measures correlates strongly with treatment efficacy or disease progression across heterogeneous patient populations and subtypes of MS. Numerous studies over the past decades have attempted to identify sensitive and specific biomarkers for diagnosis, prognosis and treatment efficacy of MS. The objective of this article is to review and discuss the current literature on body fluid biomarkers in MS, including research on potential biomarker candidates in the areas of miRNA, mRNA, lipids and proteins.

Workforce preparation: the Biohealth computing model for Master and PhD students

The article addresses the strategic role of workforce preparation in the process of adoption of Systems Medicine as a driver of biomedical research in the new health paradigm. It reports on relevant initiatives, like CASyM, fostering Systems Medicine at EU level. The chapter focuses on the BioHealth Computing Program as a reference for multidisciplinary training of future systems-oriented researchers describing the productive interactions with the Synergy-COPD project.

Lipids and lipid-reactive antibodies as biomarkers for multiple sclerosis

Multiple sclerosis (MS) is an autoimmune disease that targets the central nervous system (CNS). MS initially follows a relapsing-remitting course (RRMS) in which acute attacks are followed by a complete recovery. Eventually, 65% of the RRMS patients go on to develop secondary progressive MS (SPMS), characterized by the progressive and irreversible accumulation of neurological disability. It has been proposed that the transition from RRMS to SPMS results from changes in the nature of the inflammatory response and the progressive accumulation of neurodegeneration. To date, however, there is no reliable method to monitor the activity of the different immune and neurodegenerative processes that contribute to MS pathology. Thus, there is a need for biomarkers useful for the diagnosis, treatment and monitoring of MS patients. In this review, we discuss the potential use of lipids and the immune response against them as biomarkers of inflammation and neurodegeneration for MS.

Anti-NMDA receptor encephalitis. The disorder, the diagnosis and the immunobiology

Anti-NMDAR encephalitis is a newly characterized syndrome with a progressive, predictable clinical course and the possibility of effective treatment. Accurate and timely diagnosis is critical to selection and implementation of treatments, and optimal patient outcomes. Outcomes are improved with early diagnosis via indirect immunofluorescence or cell-based assays, and the rapid and appropriate administration of immunosuppressant and anti-psychotic therapies. Three possible scenarios accounting for the immunopathogenesis of anti-NMDAR encephalitis are presented, with the most probable one being that of paraneoplastic autoimmunity. Future efforts in this disorder should focus on elucidating the mechanisms that contribute to initiation of this antibody response, as well as exploring the role of tumors, infectious triggers and immune-reactivation. Finally, accessible tools need to be developed that allow for reliable identification of specific antibody markers against synaptic proteins.

Analysis of the mitochondrial proteome in multiple sclerosis cortex

Mitochondrial dysfunction has been proposed to play a role in the neuropathology of multiple sclerosis (MS). Previously, we reported significant alterations in the transcription of nuclear-encoded electron transport chain genes in MS and confirmed translational alterations for components of Complexes I and III that resulted in reductions in their activity. To more thoroughly and efficiently elucidate potential alterations in the expression of mitochondrial and related proteins, we have characterized the mitochondrial proteome in postmortem MS and control cortex using Surface-Enhanced Laser Desorption Ionization Time of Flight Mass Spectrometry (SELDI-TOF-MS). Using principal component analysis (PCA) and hierarchical clustering techniques we were able to analyze the differential patterns of SELDI-TOF spectra to reveal clusters of peaks which distinguished MS from control samples. Four proteins in particular were responsible for distinguishing disease from control. Peptide fingerprint mapping unambiguously identified these differentially expressed proteins. Three proteins identified are involved in respiration including cytochrome c oxidase subunit 5b (COX5b), the brain specific isozyme of creatine kinase, and hemoglobin β-chain. The fourth protein identified was myelin basic protein (MBP). We then investigated whether these alterations were consistent in the experimental autoimmune encephalomyelitis (EAE) mouse model of MS. We found that MBP was similarly altered in EAE but the respiratory proteins were not. These data indicate that while the EAE mouse model may mimic aspects of MS neuropathology which result from inflammatory demyelinating events, there is another distinct mechanism involved in mitochondrial dysfunction in gray matter in MS which is not modeled in EAE.

Metabonomic studies of schizophrenia and psychotropic medications: focus on alterations in CNS energy homeostasis

Schizophrenia is a severe neuropsychiatric disorder with a poorly understood etiology and progression. We and other research groups have found that energy metabolic pathways in the CNS are perturbed in many subjects with this disorder. Antipsychotic drugs that generally target neurotransmission are currently used for clinical management of the disorder, although these can also have marked effects on energy metabolism in the CNS and periphery. Recent proteomic and metabonomic studies have shown that molecular pathways associated with brain energy metabolism are altered in both the disorder and by antipsychotic treatments. This review focuses on discussion of these molecular alterations. Increased knowledge in this area could facilitate biomarker identification and drug discovery based on improving brain energy metabolism in this debilitating disorder.

[Molecular network analysis of multiple sclerosis brain lesion proteome]

A recent proteomics study of multiple sclerosis (MS) brain lesion-specific proteome profiling clearly revealed a pivotal role of coagulation cascade proteins in chronic active demyelination (Han MH et al. Nature 451 : 1076-1081, 2008). However, among thousands of proteins identified, nearly all of remaining proteins were left behind to be characterized in terms of their implications in MS brain lesion development. By the systems biology approach using four different pathway analysis tools of bioinformatics, we studied molecular networks and pathways of the proteome dataset of acute plaque (AP), chronic active plaque (CAP), and chronic plaque (CP). The database search on KEGG and PANTHER indicated the relevance of extracellular matrix (ECM)-mediated focal adhesion and integrin signaling to CAP and CP proteome. IPA identified the network constructed with a wide range of ECM components as one of the networks highly relevant to CAP proteome. KeyMolnet disclosed a central role of the complex interaction among diverse cytokine signaling pathways in brain lesion development at all disease stages, as well as a role of integrin signaling in CAP and CP. Although four distinct platforms produced diverse results, they commonly suggested a role of ECM and integrin signaling in development of chronic lesions of MS. These observations indicate that the selective blockade of the interaction between ECM and integrins would be a rational approach for designing inhibitors of chronic inflammatory demyelination in MS brain lesions.

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.

Genomics and proteomics: Applications in autoimmune diseases

Tremendous progress has been made over the past decade in the development and refinement of genomic and proteomic technologies for the identification of novel drug targets and molecular signatures associated with clinically important disease states, disease subsets, or differential responses to therapies. The rapid progress in high-throughput technologies has been preceded and paralleled by the elucidation of cytokine networks, followed by the stepwise clinical development of pathway-specific biological therapies that revolutionized the treatment of autoimmune diseases. Together, these advances provide opportunities for a long-anticipated personalized medicine approach to the treatment of autoimmune disease. The ever-increasing numbers of novel, innovative therapies will need to be harnessed wisely to achieve optimal long-term outcomes in as many patients as possible while complying with the demands of health authorities and health care providers for evidence-based, economically sound prescription of these expensive drugs. Genomic and proteomic profiling of patients with autoimmune diseases holds great promise in two major clinical areas: (1) rapid identification of new targets for the development of innovative therapies and (2) identification of patients who will experience optimal benefit and minimal risk from a specific (targeted) therapy. In this review, we attempt to capture important recent developments in the application of genomic and proteomic technologies to translational research by discussing informative examples covering a diversity of autoimmune diseases.

Molecular network of the comprehensive multiple sclerosis brain-lesion proteome

BACKGROUND

A recent proteomics study of multiple sclerosis (MS) lesion-specific proteome profiling clearly revealed a pivotal role of coagulation cascade proteins in chronic active demyelination. However, among thousands of proteins examined, nearly all of remaining proteins are yet to be characterized in terms of their implications in MS brain-lesion development.

METHODS

By the systems biology approach using four different pathway analysis tools of bioinformatics, we studied molecular networks and pathways of the proteome dataset of acute plaques, chronic active plaques (CAP), and chronic plaques (CP).

RESULTS

The database search on Kyoto Encyclopedia of Genes and Genomes (KEGG) and protein analysis through evolutionary relationships (PANTHER) indicated the relevance of extracellular matrix (ECM)-mediated focal adhesion and integrin signaling to CAP and CP proteome. KeyMolnet disclosed a central role of the complex interaction among diverse cytokine signaling pathways in brain-lesion development at all disease stages, as well as a role of integrin signaling in CAP and CP. Ingenuity pathway analysis (IPA) identified the network constructed with a wide range of ECM components, such as collagen, type I alpha1, type I alpha2, type VI alpha2, type VI alpha3, fibronectin 1, fibulin 2, laminin alpha1, vitronectin, and heparan sulfate proteoglycan, as one of the networks highly relevant to CAP proteome.

CONCLUSIONS

Although four distinct platforms produced diverse results, they commonly suggested a role of ECM and integrin signaling in development of chronic lesions of MS. These in silico observations indicate that the selective blockade of the interaction between ECM and integrins in brain lesions in situ would be a target for therapeutic intervention in MS.

Quantitative proteomic analysis of the cerebrospinal fluid of patients with multiple sclerosis

The diagnosis of multiple sclerosis (MS) is challenging for the lack of a specific diagnostic test. Recent researches in quantitative proteomics, however, offer new opportunities for biomarker discovery and the study of disease pathogenesis. To find more potential protein biomarkers, we used two technologies, 2-dimensional fluorescence difference in-gel electrophoresis (2D-DIGE), followed by matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) and ultra-performance liquid chromato-graph coupled with quadrupole time-of-flight mass spectrometry (UPLC/Q-TOF MS), to quantitatively analyse differential proteomic expression in the cerebrospinal fluid (CSF) between patients with MS (the experiment group) and patients with other neurological diseases (ONDs; the control group). Analysis by the former technology identified more than 43 different protein spots (39 proteins), of which 17 spots (13 proteins) showed more than 1.5-fold difference in abundance as analysed by DeCyder software (GE Healthcare, Piscataway. NJ, USA) between the MS and the ONDs groups. The expression of five protein spots was elevated and the expression of 12 protein spots was decreased in the MS group. Meanwhile, the latter method, UPLC/Q-TOF MS showed 68 different proteins. There were 45 proteins with a difference of more than 1.5 folds between the two groups, in which the expression of 20 proteins was elevated and the expression of 25 proteins was decreased in the MS group. Data provided by the two methods indicated that the proteins overlapped ratio was 27% in the 26 significant proteins that had the same regulation tendency. The differential CSF proteins were analysed further by biological network and it revealed interaction of them. The subsequent ELISA measuring the concentration of cystatin C (P < 0.01), which was one of the proteins discovered simultaneously with the two technologies, confirmed the results of the two quantitative proteomic analysis. The combination of the two quantitative proteomic technologies was helpful in discovering differentially expressed proteins that may have a connection with MS disease physiology and serve as useful biomarkers for diagnosis and treatment of MS diseases.

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.

Integrating post-genomic approaches as a strategy to advance our understanding of health and disease

Following the publication of the complete human genomic sequence, the post-genomic era is driven by the need to extract useful information from genomic data. Genomics, transcriptomics, proteomics, metabolomics, epidemiological data and microbial data provide different angles to our understanding of gene-environment interactions and the determinants of disease and health. Our goal and our challenge are to integrate these very different types of data and perspectives of disease into a global model suitable for dissecting the mechanisms of disease and for predicting novel therapeutic strategies. This review aims to highlight the need for and problems with complex data integration, and proposes a framework for data integration. While there are many obstacles to overcome, biological models based upon multiple datasets will probably become the basis that drives future biomedical research.

Biomarkers for Parkinson's disease

With the advent of systems biological concepts there has been a surge of interest in biological factors, or biomarkers that can be measured and which allow the identification of individuals at risk. Biomarkers for Parkinson's disease have been identified which provide evidence of systemic metabolic dysregulation in this disorder. Such biomarkers can be studied in blood, serum and plasma but also in CSF and urine, and the study by Hoepken et al. in this issue has even made use of skin fibroblasts. The authors report on the induction of alpha-synuclein expression and suggest that the expression changes described might potentially allow objective PD patient diagnosis in an accessible, peripheral tissue. This mini-review aims to provide a broader perspective on PD functional genomics and seeks to illustrate in a systems biological context why the findings by Hoepken and colleagues are of clinical significance.

HMDB: a knowledgebase for the human metabolome

The Human Metabolome Database (HMDB, http://www.hmdb.ca) is a richly annotated resource that is designed to address the broad needs of biochemists, clinical chemists, physicians, medical geneticists, nutritionists and members of the metabolomics community. Since its first release in 2007, the HMDB has been used to facilitate the research for nearly 100 published studies in metabolomics, clinical biochemistry and systems biology. The most recent release of HMDB (version 2.0) has been significantly expanded and enhanced over the previous release (version 1.0). In particular, the number of fully annotated metabolite entries has grown from 2180 to more than 6800 (a 300% increase), while the number of metabolites with biofluid or tissue concentration data has grown by a factor of five (from 883 to 4413). Similarly, the number of purified compounds with reference to NMR, LC-MS and GC-MS spectra has more than doubled (from 380 to more than 790 compounds). In addition to this significant expansion in database size, many new database searching tools and new data content has been added or enhanced. These include better algorithms for spectral searching and matching, more powerful chemical substructure searches, faster text searching software, as well as dedicated pathway searching tools and customized, clickable metabolic maps. Changes to the user-interface have also been implemented to accommodate future expansion and to make database navigation much easier. These improvements should make the HMDB much more useful to a much wider community of users.