Integrative analysis of Multiple Sclerosis using a systems biology approach

Gene network reveals LASP1, TUBA1C, and S100A6 are likely playing regulatory roles in multiple sclerosis

Introduction

Multiple sclerosis (MS), a non-contagious and chronic disease of the central nervous system, is an unpredictable and indirectly inherited disease affecting different people in different ways. Using Omics platforms genomics, transcriptomics, proteomics, epigenomics, interactomics, and metabolomics database, it is now possible to construct sound systems biology models to extract full knowledge of the MS and recognize the pathway to uncover the personalized therapeutic tools.

Methods

In this study, we used several Bayesian Networks in order to find the transcriptional gene regulation networks that drive MS disease. We used a set of BN algorithms using the R add-on package bnlearn. The BN results underwent further downstream analysis and were validated using a wide range of Cytoscape algorithms, web based computational tools and qPCR amplification of blood samples from 56 MS patients and 44 healthy controls. The results were semantically integrated to improve understanding of the complex molecular architecture underlying MS, distinguishing distinct metabolic pathways and providing a valuable foundation for the discovery of involved genes and possibly new treatments.

Results

Results show that the LASP1, TUBA1C, and S100A6 genes were most likely playing a biological role in MS development. Results from qPCR showed a significant increase (P < 0.05) in LASP1 and S100A6 gene expression levels in MS patients compared to that in controls. However, a significant down regulation of TUBA1C gene was observed in the same comparison.

Conclusion

This study provides potential diagnostic and therapeutic biomarkers for enhanced understanding of gene regulation underlying MS.

Subtypes of relapsing-remitting multiple sclerosis identified by network analysis

We used network analysis to identify subtypes of relapsing-remitting multiple sclerosis subjects based on their cumulative signs and symptoms. The electronic medical records of 113 subjects with relapsing-remitting multiple sclerosis were reviewed, signs and symptoms were mapped to classes in a neuro-ontology, and classes were collapsed into sixteen superclasses by subsumption. After normalization and vectorization of the data, bipartite (subject-feature) and unipartite (subject-subject) network graphs were created using NetworkX and visualized in Gephi. Degree and weighted degree were calculated for each node. Graphs were partitioned into communities using the modularity score. Feature maps visualized differences in features by community. Network analysis of the unipartite graph yielded a higher modularity score (0.49) than the bipartite graph (0.25). The bipartite network was partitioned into five communities which were named fatigue, behavioral, hypertonia/weakness, abnormal gait/sphincter, and sensory, based on feature characteristics. The unipartite network was partitioned into five communities which were named fatigue, pain, cognitive, sensory, and gait/weakness/hypertonia based on features. Although we did not identify pure subtypes (e.g., pure motor, pure sensory, etc.) in this cohort of multiple sclerosis subjects, we demonstrated that network analysis could partition these subjects into different subtype communities. Larger datasets and additional partitioning algorithms are needed to confirm these findings and elucidate their significance. This study contributes to the literature investigating subtypes of multiple sclerosis by combining feature reduction by subsumption with network analysis.

Evaluating the Utility of Proteomics for the Identification of Circulating Pharmacodynamic Biomarkers of IFNβ-1a Biologics

Proteomics has the potential to identify pharmacodynamic (PD) biomarkers for similarity assessment of proposed biosimilars without relying on clinical efficacy end points. In this study, with 36 healthy participants randomized to therapeutic doses of interferon-beta 1a products (IFNβ-1a) or pegylated-IFNβ-1a (pegIFNβ-1a) approved to treat multiple sclerosis or placebo, we evaluated the utility of a proteomic assay that profiles > 7,000 plasma proteins. IFNβ-1a and pegIFNβ-1a resulted in 248 and 528 differentially expressed protein analytes, respectively, between treatment and placebo groups over the time course. Thirty-one proteins were prioritized based on a maximal fold change ≥ 2 from baseline, baseline adjusted area under the effect curve (AUEC) and overlap between the 2 products. Of these, the majority had a significant AUEC compared with placebo in response to either product; 8 proteins showed > 4-fold maximal change from baseline. We identified previously reported candidates, beta-2microglobulin and interferon-induced GTP-binding protein (Mx1) with ~ 50% coefficient of variation (CV) for AUEC, and many new candidates (including I-TAC, C1QC, and IP-10) with CVs ranging from 26%-129%. Upstream regulator analysis of differentially expressed proteins predicted activation of IFNβ1 signaling as well as other cytokine, enzyme, and transcription signaling networks by both products. Although independent replication is required to confirm present results, our study demonstrates the utility of proteomics for the identification of individual and composite candidate PD biomarkers that may be leveraged to support clinical pharmacology studies for biosimilar approvals, especially when biologics have complex mechanisms of action or do not have previously characterized PD biomarkers.

Multiple sclerosis and computational biology (Review)

Multiple sclerosis (MS) is an autoimmune neurodegenerative disease whose prevalence has increased worldwide. The resultant symptoms may be debilitating and can substantially reduce the of patients. Computational biology, which involves the use of computational tools to answer biomedical questions, may provide the basis for novel healthcare approaches in the context of MS. The rapid accumulation of health data, and the ever-increasing computational power and evolving technology have helped to modernize and refine MS research. From the discovery of novel biomarkers to the optimization of treatment and a number of quality-of-life enhancements for patients, computational biology methods and tools are shaping the field of MS diagnosis, management and treatment. The final goal in such a complex disease would be personalized medicine, i.e., providing healthcare services that are tailored to the individual patient, in accordance to the particular biology of their disease and the environmental factors to which they are subjected. The present review article summarizes the current knowledge on MS, modern computational biology and the impact of modern computational approaches of MS.

Modelling liver cancer microenvironment using a novel 3D culture system

The tumor microenvironment and its contribution to tumorigenesis has been a focal highlight in recent years. A two-way communication between the tumor and the surrounding microenvironment sustains and contributes to the growth and metastasis of tumors. Progression and metastasis of hepatocellular carcinoma (HCC) have been reported to be exceedingly influenced by diverse microenvironmental cues. In this study, we present a 3D-culture model of liver cancer to better mimic in vivo tumor settings. By creating novel 3D co-culture model that combines free-floating and scaffold-based 3D-culture techniques of liver cancer cells and fibroblasts, we aimed to establish a simple albeit reproducible ex vivo cancer microenvironment model that captures tumor-stroma interactions. The model presented herein exhibited unique gene expression and protein expression profiles when compared to 2D and 3D mono-cultures of liver cancer cells. Our results showed that in vivo like conditions cannot be mimicked by simply growing cancer cells as spheroids, but by co-culturing them with 3D fibroblast with which they were able to crosstalk. This was evident by the upregulation of several pathways involved in HCC, and the increase in secreted factors by co-cultured cancer cells, many of which are also involved in tumor-stroma interactions. Compared to the conventional 2D culture, the proposed model exhibits an increase in the expression of genes associated with development, progression, and poor prognosis of HCC. Our results correlated with an aggressive outcome that better mirrors in vivo HCC, and therefore, a more reliable platform for molecular understanding of HCC.

Multiple Sclerosis Biomarker Candidates Revealed by Cell-Type-Specific Interactome Analysis

Multiple sclerosis (MS) is a demyelinating disorder that affects multiple regions of the central nervous system such as the brain, spinal cord, and optic nerves. Susceptibility to MS, as well as disease progression rates, displays marked patient-to-patient variability. To date, biomarkers that forecast differences in clinical phenotypes and outcomes have been limited. In this context, cell-type-specific interactome analyses offer important prospects and hope for novel diagnostics and therapeutics. We report here an original study using bioinformatic analysis of MS data sets that revealed interaction profiles as well as specific hub proteins in white matter (WM) and gray matter (GM) that appear critical for disease mechanisms. First, cell-type-specific interactome analyses suggested that while interactions within the WM were focused on oligodendrocytes, interactions within the GM were mostly neuron centric. Second, hub proteins such as APP, EGLN3, PTEN, and LRRK2 were identified to be differentially regulated in MS data sets. Lastly, a comparison of the brain and peripheral blood samples identified biomarker candidates such as NRGN, CRTC1, CDC42, and IFITM3 to be differentially expressed in different types of MS. These findings offer a unique cell-type-specific cell-to-cell interaction network in MS and identify potential biomarkers by comparative analysis of the brain and the blood transcriptomics. From a study design and methodology perspective, we suggest that the cell-type-specific interactome analysis is an important systems science frontier that might offer new insights on other neurodegenerative and brain disorders as well.

A Final Frontier in Environment-Genome Interactions? Integrated, Multi-Omic Approaches to Predictions of Non-Communicable Disease Risk

Epidemiological and associative research from humans and animals identifies correlations between the environment and health impacts. The environment—health inter-relationship is effected through an individual’s underlying genetic variation and mediated by mechanisms that include the changes to gene regulation that are associated with the diversity of phenotypes we exhibit. However, the causal relationships have yet to be established, in part because the associations are reduced to individual interactions and the combinatorial effects are rarely studied. This problem is exacerbated by the fact that our genomes are highly dynamic; they integrate information across multiple levels (from linear sequence, to structural organisation, to temporal variation) each of which is open to and responds to environmental influence. To unravel the complexities of the genomic basis of human disease, and in particular non-communicable diseases that are also influenced by the environment (e.g., obesity, type II diabetes, cancer, multiple sclerosis, some neurodegenerative diseases, inflammatory bowel disease, rheumatoid arthritis) it is imperative that we fully integrate multiple layers of genomic data. Here we review current progress in integrated genomic data analysis, and discuss cases where data integration would lead to significant advances in our ability to predict how the environment may impact on our health. We also outline limitations which should form the basis of future research questions. In so doing, this review will lay the foundations for future research into the impact of the environment on our health.

Integrative OMICS Data-Driven Procedure Using a Derivatized Meta-Analysis Approach

The wealth of high-throughput data has opened up new opportunities to analyze and describe biological processes at higher resolution, ultimately leading to a significant acceleration of scientific output using high-throughput data from the different omics layers and the generation of databases to store and report raw datasets. The great variability among the techniques and the heterogeneous methodologies used to produce this data have placed meta-analysis methods as one of the approaches of choice to correlate the resultant large-scale datasets from different research groups. Through multi-study meta-analyses, it is possible to generate results with greater statistical power compared to individual analyses. Gene signatures, biomarkers and pathways that provide new insights of a phenotype of interest have been identified by the analysis of large-scale datasets in several fields of science. However, despite all the efforts, a standardized regulation to report large-scale data and to identify the molecular targets and signaling networks is still lacking. Integrative analyses have also been introduced as complementation and augmentation for meta-analysis methodologies to generate novel hypotheses. Currently, there is no universal method established and the different methods available follow different purposes. Herein we describe a new unifying, scalable and straightforward methodology to meta-analyze different omics outputs, but also to integrate the significant outcomes into novel pathways describing biological processes of interest. The significance of using proper molecular identifiers is highlighted as well as the potential to further correlate molecules from different regulatory levels. To show the methodology’s potential, a set of transcriptomic datasets are meta-analyzed as an example.

Identification of viral-mediated pathogenic mechanisms in neurodegenerative diseases using network-based approaches

During the course of a viral infection, virus-host protein-protein interactions (PPIs) play a critical role in allowing viruses to replicate and survive within the host. These interspecies molecular interactions can lead to viral-mediated perturbations of the human interactome causing the generation of various complex diseases. Evidences suggest that viral-mediated perturbations are a possible pathogenic etiology in several neurodegenerative diseases (NDs). These diseases are characterized by chronic progressive degeneration of neurons, and current therapeutic approaches provide only mild symptomatic relief; therefore, there is unmet need for the discovery of novel therapeutic interventions. In this paper, we initially review databases and tools that can be utilized to investigate viral-mediated perturbations in complex NDs using network-based analysis by examining the interaction between the ND-related PPI disease networks and the virus-host PPI network. Afterwards, we present our theoretical-driven integrative network-based bioinformatics approach that accounts for pathogen-genes-disease-related PPIs with the aim to identify viral-mediated pathogenic mechanisms focusing in multiple sclerosis (MS) disease. We identified seven high centrality nodes that can act as disease communicator nodes and exert systemic effects in the MS-enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways network. In addition, we identified 12 KEGG pathways, 5 Reactome pathways and 52 Gene Ontology Immune System Processes by which 80 viral proteins from eight viral species might exert viral-mediated pathogenic mechanisms in MS. Finally, our analysis highlighted the Th17 differentiation pathway, a disease communicator node and part of the 12 underlined KEGG pathways, as a key viral-mediated pathogenic mechanism and a possible therapeutic target for MS disease.

Analysis of association between MALAT1 haplotype and the severity of normal-tension glaucoma (NTG)

MALAT1, which is disorderly expressed in the growth, invasion, migration and cancer cell apoptosis, was shown to be associated with normal-tension glaucoma (NTG), a type of optic neuropathy. The haplotype in MALAT1 affects its expression and is correlated with human diseases like normal-tension glaucoma (NTG). However, the underlying detailed mechanism remains unclear. In this study, we aimed to analyse the association between MALAT1 haplotype and the severity of NTG in a molecular level. Quantitative real-time PCR, ELISA and luciferase assays were performed to establish the underlying signalling pathways. RNFL thickness, RA and C/D ratio were calculated for NTG patients. Accordingly, GGGT haplotype was demonstrated to be associated with a decreased risk of NTG. The MALAT1 level in serum of NTG patients carrying GGGT haplotype was significantly decreased compared with NTG patients carrying other haplotypes, along with elevated miR-1 expression and diminished IL-6 expression. NTG patients carrying GGGT haplotype had thicker RNFL and RA, but a smaller C/D ratio. Sequence analysis found potential target sites of miR-1 on MALAT1 and IL-6, and luciferase assay confirmed the inhibitory effect of miR-1 on MALAT1 and IL-6 expression. Meanwhile, MALAT1 also down-regulated miR-1 expression and consequently up-regulated IL-6 expression. This study presented evidence for a regulatory network containing MALAT1, miR-1 and IL-6, and further demonstrated the effect of MALAT1 haplotype on the risk and severity of NTG.

The Role of Hemoglobin Subunit Delta in the Immunopathy of Multiple Sclerosis: Mitochondria Matters

Background

Although the exact pathophysiology of MS has not been identified, mitochondrial stress can be one of the culprits in MS development. Herein, we have applied microarray analysis, single-cell sequencing analysis, and ex vivo study to elucidate the role of mitochondrial stress in PBMCs of MS patients.

Methods

For this purpose, we analyzed the GSE21942 and GSE138266 datasets to identify the DEGs and hub genes in the PBMCS of MS patients and describe the expression of shared genes in the different immune cells. The GO pathway analysis of DEGs and turquoise module genes were conducted to shed light on their biological significance. To validate the obtained results, the gene expression of HBD, as the most remarkable DEG in the PBMCS of affected patients, was measured in the PBMCS of healthy donors, treatment-naïve MS patients, and MS patients treated with GA, fingolimod, DMF, and IFNβ-1α.

Results

Based on WGCNA and DEGs analysis, HBD, HBM, SLC4A1, LILRA5, SLC25A37, SELENBP1, ALYREF, SNRNP40, and HINT3 are the identified common genes in the PMBCS. Using single-cell sequencing analysis on PBMCS, we have characterized various cell populations in MS and illustrated the common gene expression on the different immune cells. Furthermore, GO pathway analysis of DEGs, and turquoise module genes have indicated that these genes are involved in immune responses, myeloid cell activation, leukocyte activation, oxygen carrier activity, and replication fork processing bicarbonate transport pathways. Our ex vivo investigation has shown that HBD expression in the treatment-naïve RRMS patients is significantly increased compared to healthy donors. Of interest, immunomodulatory therapies with fingolimod, DMF, and IFNβ-1α have significantly decreased HBD expression.

Conclusion

HBD is one of the remarkably up-regulated genes in the PBMCS of MS patients. HBD is substantially up-regulated in treatment-naïve MS patients, and immunomodulatory therapies with fingolimod, DMF, and IFNβ-1α can remarkably down-regulate HBD expression. Based on the currently available evidence, the cytoprotective nature of HBD against oxidative stress can be the underlying reason for HBD up-regulation in MS. Nevertheless, further investigations are needed to shed light on the molecular mechanisms of HBD in the oxidative stress of MS patients.

Genomic, proteomic, and systems biology approaches in biomarker discovery for multiple sclerosis

Multiple sclerosis (MS) is a neuroinflammatory disorder characterized by autoimmune-mediated inflammatory lesions in CNS leading to myelin damage and axonal loss. MS is a heterogenous disease with variable and unpredictable disease course. Due to its complex nature, MS is difficult to diagnose and responses to specific treatments may vary between individuals. Therefore, there is an indisputable need for biomarkers for early diagnosis, prediction of disease exacerbations, monitoring the progression of disease, and for measuring responses to therapy. Genomic and proteomic studies have sought to understand the molecular basis of MS and find biomarker candidates. Advances in next-generation sequencing and mass-spectrometry techniques have yielded an unprecedented amount of genomic and proteomic data; yet, translation of the results into the clinic has been underwhelming. This has prompted the development of novel data science techniques for exploring these large datasets to identify biologically relevant relationships and ultimately point towards useful biomarkers. Herein we discuss optimization of omics study designs, advances in the generation of omics data, and systems biology approaches aimed at improving biomarker discovery and translation to the clinic for MS.

Blood platelet RNA enables the detection of multiple sclerosis

Background

In multiple sclerosis (MS), clinical assessment, MRI and cerebrospinal fluid are important in the diagnostic process. However, no blood biomarker has been confirmed as a useful tool in the diagnostic work-up.

Objectives

Blood platelets contain a rich spliced mRNA repertoire that can alter during megakaryocyte development but also during platelet formation and platelet circulation. In this proof of concept study, we evaluate the diagnostic potential of spliced blood platelet RNA for the detection of MS.

Methods

We isolated and sequenced platelet RNA of blood samples obtained from 57 MS patients and 66 age- and gender-matched healthy controls (HCs). 60% was used to develop a particle swarm-optimized (PSO) support vector machine classification algorithm. The remaining 40% served as an independent validation series.

Results

In total, 1249 RNAs with differential spliced junction expression levels were identified between platelets of MS patients as compared to HCs, including EPSTI1, IFI6, and RPS6KA3, in line with reported inflammatory signatures in the blood of MS patients. The RNAs were subsequently used as input for a MS classifier, capable of detecting MS with 80% accuracy in the independent validation series.

Conclusions

Spliced platelet RNA may enable the blood-based diagnosis of MS, warranting large-scale validation.

Early Diagnosis of Multiple Sclerosis Based on Optical and Electrochemical Biosensors: Comprehensive Perspective

Background:

Multiple Sclerosis (MS) involves an immune-mediated response in which body’s immune system destructs the protective sheath (myelin). Part of the known MS biomarkers are discovered in cerebrospinal fluid like oligoclonal lgG (OCGB), and also in blood like myelin Oligodendrocyte Glycoprotein (MOG). The conventional MS diagnostic methods often fail to detect the disease in early stages such as Clinically Isolated Syndrome (CIS), which considered as a concerning issue since CIS highlighted as a prognostic factor of MS development in most cases.

Methods:

MS diagnostic techniques include Magnetic Resonance Imaging (MRI) of the brain and spinal cord, lumbar puncture (or spinal tap) that evaluate cerebrospinal fluid, evoked potential testing revealing abnormalities in the brain and spinal cord. These conventional diagnostic methods have some negative points such as extensive processing time as well as restriction in the quantity of samples that can be analyzed concurrently. Scientists have focused on developing the detection methods especially early detection which belongs to ultra-sensitive, non-invasive and needed for the Point of Care (POC) diagnosis because the situation was complicated by false positive or negative results.

Results:

As a result, biosensors are utilized and investigated since they could be ultra-sensitive to specific compounds, cost effective devices, body-friendly and easy to implement. In addition, it has been proved that the biosensors on physiological fluids (blood, serum, urine, saliva, milk etc.) have quick response in a non-invasive rout. In general form, a biosensor system for diagnosis and early detection process usually involves; biomarker (target molecule), bio receptor (recognition element) and compatible bio transducer.

Conclusion:

Studies underlined that early treatment of patients with high possibility of MS can be advantageous by postponing further abnormalities on MRI and subsequent attacks.

:

This Review highlights variable disease diagnosis approaches such as Surface Plasmon Resonance (SPR), electrochemical biosensors, Microarrays and microbeads based Microarrays, which are considered as promising methods for detection and early detection of MS.

A potential association between IL-3 and type I and III interferons in systemic lupus erythematosus

Objectives

Plasmacytoid dendritic cells (pDCs), through the production of type 1 interferons (IFNs) and other cytokines, are major contributors to systemic lupus erythematosus (SLE) pathogenesis. IL-3 promotes pDC survival, but its role in SLE is not well characterised. This study investigated serum IL-3 and IFN levels, and a whole blood 'IL-3 gene signature', in human SLE.

Methods

Serum cytokine levels were measured by ELISA in n = 42 SLE patients, and n = 44 healthy donors. IL-3-regulated genes were determined by RNASeq of healthy donor whole blood cells (WBCs) stimulated in vitro with IL-3 for 6 or 24 h. Whole blood cell RNASeq analysis was undertaken in a separate cohort of n = 31 SLE patients, and n = 28 healthy donors.

Results

Serum IL-3 levels correlated with IFNα (r = 0.612, 95% CI 0.455-0.733, P < 0.001) and type III IFN (r = 0.585, 95% CI 0.406-0.720, P < 0.0001). IL-3 stimulation of WBC in vitro altered 794 genes (-1 ≥ logFC ≥ 1, FDR < 0.05), of which 35 overlapped with genes differentially expressed between SLE and healthy donors. These 35 genes were expressed in 27/31 SLE donors, revealing the presence of an 'IL-3 gene signature'. There was strong correlation between the IL-3 signature and an IFN signature, as determined by hierarchical clustering of the 500 most variable genes in SLE donors (r = 0.939, 95% CI 0.898-0.964, P < 0.0001).

Conclusion

A dual IL-3/IFN gene signature is a feature of SLE. An association between IL-3 and IFN raises the possibility that dual blockade of IL-3 and IFN may be especially useful for SLE patients with this dual cytokine gene signature.

An integrative methodology based on protein-protein interaction networks for identification and functional annotation of disease-relevant genes applied to channelopathies

Background

Biologically data-driven networks have become powerful analytical tools that handle massive, heterogeneous datasets generated from biomedical fields. Protein-protein interaction networks can identify the most relevant structures directly tied to biological functions. Functional enrichments can then be performed based on these structural aspects of gene relationships for the study of channelopathies. Channelopathies refer to a complex group of disorders resulting from dysfunctional ion channels with distinct polygenic manifestations. This study presents a semi-automatic workflow using protein-protein interaction networks that can identify the most relevant genes and their biological processes and pathways in channelopathies to better understand their etiopathogenesis. In addition, the clinical manifestations that are strongly associated with these genes are also identified as the most characteristic in this complex group of diseases.

Results

In particular, a set of nine representative disease-related genes was detected, these being the most significant genes in relation to their roles in channelopathies. In this way we attested the implication of some voltage-gated sodium (SCN1A, SCN2A, SCN4A, SCN4B, SCN5A, SCN9A) and potassium (KCNQ2, KCNH2) channels in cardiovascular diseases, epilepsies, febrile seizures, headache disorders, neuromuscular, neurodegenerative diseases or neurobehavioral manifestations. We also revealed the role of Ankyrin-G (ANK3) in the neurodegenerative and neurobehavioral disorders as well as the implication of these genes in other systems, such as the immunological or endocrine systems.

Conclusions

This research provides a systems biology approach to extract information from interaction networks of gene expression. We show how large-scale computational integration of heterogeneous datasets, PPI network analyses, functional databases and published literature may support the detection and assessment of possible potential therapeutic targets in the disease. Applying our workflow makes it feasible to spot the most relevant genes and unknown relationships in channelopathies and shows its potential as a first-step approach to identify both genes and functional interactions in clinical-knowledge scenarios of target diseases.

Methods

An initial gene pool is previously defined by searching general databases under a specific semantic framework. From the resulting interaction network, a subset of genes are identified as the most relevant through the workflow that includes centrality measures and other filtering and enrichment databases.

Gene Regulatory Networks in Peripheral Mononuclear Cells Reveals Critical Regulatory Modules and Regulators of Multiple Sclerosis

Multiple sclerosis (MS) is a complex, demyelinating disease with the involvement of autoimmunity and neurodegeneration. Increasing efforts have been made towards identifying the diagnostic markers to differentiate the classes of MS from other similar neurological conditions. Using a systems biology approach, we constructed four types of gene regulatory networks (GRNs) involved in peripheral blood mononuclear cells (PBMCs). The regulatory strength of each GRN across primary progressive MS (PPMS), relapsing-remitting MS (RRMS), secondary progressive MS (SPMS), and control were evaluated by an integrity algorithm. Among the constructed GRNs (referred as TF_gene_miRNA), POU3F2_CDK6_hsa-miR-590-3p, MEIS1_CASC3_hsa-miR-1261, STAT3_OGG1_hsa-miR-298, and TCF4_FMR1_hsa-miR-301b were top-ranked and differentially regulated in all classes of MS compared to control. These GRNs showed potential involvement in regulating various molecular pathways such as interleukin, integrin, glypican, sphingosine phosphate, androgen, and Wnt signaling pathways. For validation, the qPCR analysis of the GRN components (TFs, gene, and miRNAs) in PBMCs of healthy controls (n = 30), RRMS (n = 14), PPMS (n = 13) and SPMS (n = 12) were carried out. Real-time expression analysis of GRNs showed a similar regulatory pattern as derived from our systems biology approach. Also, our study provided several novel GRNs that regulate unique and common molecular mechanisms between MS conditions. Hence, these regulatory components of GRNs will help to understand the disease mechanism across MS classes and further insight may though light towards diagnosis.

A cell type-specific transcriptomic approach to map B cell and monocyte type I interferon-linked pathogenic signatures in Multiple Sclerosis

Alteration in endogenous Interferon (IFN) system may profoundly impact immune cell function in autoimmune diseases. Here, we provide evidence that dysregulation in IFN-regulated genes and pathways are involved in B cell- and monocyte-driven pathogenic contribution to Multiple Sclerosis (MS) development and maintenance. In particular, by using an Interferome-based cell type-specific approach, we characterized an increased susceptibility to an IFN-linked caspase-3 dependent apoptotic cell death in both B cells and monocytes of MS patients that may arise from their chronic activation and persistent stimulation by activated T cells. Ongoing caspase-3 activation functionally impacts on MS monocyte properties influencing the STAT-3/IL-16 axis, thus, driving increased expression and massive release of the bio-active IL-16 triggering and perpetuating CD4⁺ T cell migration. Importantly, our analysis also identified a previously unknown multi-component defect in type I IFN-mediated signaling and response to virus pathways specific of MS B cells, impacting on induction of anti-viral responses and Epstein-barr virus infection control in patients. Taking advantage of cell type-specific transcriptomics and in-depth functional validation, this study revealed pathogenic contribution of endogenous IFN signaling and IFN-regulated cell processes to MS pathogenesis with implications on fate and functions of B cells and monocytes that may hold therapeutic potential.

Emerging small-molecule treatments for multiple sclerosis: focus on B cells

Multiple sclerosis (MS) is a major cause of disability in young adults. Following an unknown trigger (or triggers), the immune system attacks the myelin sheath surrounding axons, leading to progressive nerve cell death. Antibodies and small-molecule drugs directed against B cells have demonstrated good efficacy in slowing progression of the disease. This review focusses on small-molecule drugs that can affect B-cell biology and may have utility in disease management. The risk genes for MS are examined from the drug target perspective. Existing small-molecule therapies for MS with B-cell actions together with new drugs in development are described. The potential for experimental molecules with B-cell effects is also considered. Small molecules can have diverse actions on B cells and be cytotoxic, anti-inflammatory and anti-viral. The current B cell-directed therapies often kill B-cell subsets, which can be effective but lead to side effects and toxicity. A deeper understanding of B-cell biology and the effect on MS disease should lead to new drugs with better selectivity, efficacy, and an improved safety profile. Small-molecule drugs, once the patent term has expired, provide a uniquely sustainable form of healthcare.

MALAT1 Genomic Variants and Risk of Multiple Sclerosis

Metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) is a long noncoding RNA (lncRNA) with a possible role in the regulation of immune responses. A previous study has demonstrated down-regulation of this lncRNA in multiple sclerosis (MS) patients. In the current study, we genotyped two MALAT1 single nucleotide polymorphisms (SNPs) in 428 Iranian MS patients and 505 healthy subjects. The G allele of the rs619586 was significantly under-represented in MS patients compared with controls (OR (95% CI) = 0.65 (0.46-0.92), adjusted P value = 0.03). This SNP was associated with lower MS risk in dominant model (OR (95% CI) = 0.63 (0.43-0.91), adjusted P value = 0.03). The rs3200401 was not associated with MS risk in any inheritance model. Moreover, the A T haplotype (rs619586 and rs3200401, respectively) within MALAT1 was associated with MS risk. The current study provides additional evidences for contribution of MALAT1 in the pathogenesis of MS.

Clinical and multi-omics cross-phenotyping of patients with autoimmune and autoinflammatory diseases: the observational TRANSIMMUNOM protocol

INTRODUCTION

Autoimmune and autoinflammatory diseases (AIDs) represent a socioeconomic burden as the second cause of chronic illness in Western countries. In this context, the TRANSIMMUNOM clinical protocol is designed to revisit the nosology of AIDs by combining basic, clinical and information sciences. Based on classical and systems biology analyses, it aims to uncover important phenotypes that cut across diagnostic groups so as to discover biomarkers and identify novel therapeutic targets.

METHODS AND ANALYSIS

TRANSIMMUNOM is an observational clinical protocol that aims to cross-phenotype a set of 19 AIDs, six related control diseases and healthy volunteers . We assembled a multidisciplinary cohort management team tasked with (1) selecting informative biological (routine and omics type) and clinical parameters to be captured, (2) standardising the sample collection and shipment circuit, (3) selecting omics technologies and benchmarking omics data providers, (4) designing and implementing a multidisease electronic case report form and an omics database and (5) implementing supervised and unsupervised data analyses.

ETHICS AND DISSEMINATION

The study was approved by the institutional review board of Pitié-Salpêtrière Hospital (ethics committee Ile-De-France 48-15) and done in accordance with the Declaration of Helsinki and good clinical practice. Written informed consent is obtained from all participants before enrolment in the study. TRANSIMMUNOM's project website provides information about the protocol (https://www.transimmunom.fr/en/) including experimental set-up and tool developments. Results will be disseminated during annual scientific committees appraising the project progresses and at national and international scientific conferences.

DISCUSSION

Systems biology approaches are increasingly implemented in human pathophysiology research. The TRANSIMMUNOM study applies such approach to the pathophysiology of AIDs. We believe that this translational systems immunology approach has the potential to provide breakthrough discoveries for better understanding and treatment of AIDs.

TRIAL REGISTRATION NUMBER

NCT02466217; Pre-results.