Multiple sclerosis (MS) is a common autoimmune disease that targets myelin in the central nervous system (CNS). Multiple genome-wide association studies (GWAS) over the past 10 years have uncovered ...more than 200 loci that independently contribute to disease pathogenesis. As with many other complex diseases, risk of developing MS is driven by multiple common variants whose biological effects are not immediately clear. Here, we present a historical perspective on the progress made in MS genetics and discuss current work geared towards creating a more complete model that accurately represents the genetic landscape of MS susceptibility. Such a model necessarily includes a better understanding of the individual contributions of each common variant to the cellular phenotypes, and interactions with other genes and with the environment. Future genetic studies in MS will likely focus on the role of rare variants and endophenotypes.
More than 200 loci have been associated with MS susceptibility to date (half of them in the past 4 years alone).
There is extensive sharing of genetic risk variants between MS and other autoimmune diseases. This suggests a model in which a general risk for autoimmunity is inherited. Additional genetic (and epigenetic) determinants and environmental exposures are compounded to ultimately define the target organ of the autodestructive process.
Efforts to characterize the biological consequences of reported associations are ongoing.
Cell-specific pathways are being developed to understand disease heterogeneity and individualized risk assessment.
The first decade of Genome Wide Association Studies (GWAS) has uncovered a wealth of disease-associated variants. Two important derivations will be the translation of this information into a ...multiscale understanding of pathogenic variants and leveraging existing data to increase the power of existing and future studies through prioritization. We explore edge prediction on heterogeneous networks--graphs with multiple node and edge types--for accomplishing both tasks. First we constructed a network with 18 node types--genes, diseases, tissues, pathophysiologies, and 14 MSigDB (molecular signatures database) collections--and 19 edge types from high-throughput publicly-available resources. From this network composed of 40,343 nodes and 1,608,168 edges, we extracted features that describe the topology between specific genes and diseases. Next, we trained a model from GWAS associations and predicted the probability of association between each protein-coding gene and each of 29 well-studied complex diseases. The model, which achieved 132-fold enrichment in precision at 10% recall, outperformed any individual domain, highlighting the benefit of integrative approaches. We identified pleiotropy, transcriptional signatures of perturbations, pathways, and protein interactions as influential mechanisms explaining pathogenesis. Our method successfully predicted the results (with AUROC = 0.79) from a withheld multiple sclerosis (MS) GWAS despite starting with only 13 previously associated genes. Finally, we combined our network predictions with statistical evidence of association to propose four novel MS genes, three of which (JAK2, REL, RUNX3) validated on the masked GWAS. Furthermore, our predictions provide biological support highlighting REL as the causal gene within its gene-rich locus. Users can browse all predictions online (http://het.io). Heterogeneous network edge prediction effectively prioritized genetic associations and provides a powerful new approach for data integration across multiple domains.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
There is emerging evidence that the commensal microbiota has a role in the pathogenesis of multiple sclerosis (MS), a putative autoimmune disease of the CNS. Here, we compared the gut microbial ...composition of 34 monozygotic twin pairs discordant for MS. While there were no major differences in the overall microbial profiles, we found a significant increase in some taxa such as Akkermansia in untreated MS twins. Furthermore, most notably, when transplanted to a transgenic mouse model of spontaneous brain autoimmunity, MS twin-derived microbiota induced a significantly higher incidence of autoimmunity than the healthy twinderived microbiota. The microbial profiles of the colonized mice showed a high intraindividual and remarkable temporal stability with several differences, including Sutterella, an organism shown to induce a protective immunoregulatory profile in vitro. Immune cells from mouse recipients of MS-twin samples produced less IL-10 than immune cells from mice colonized with healthy-twin samples. IL-10 may have a regulatory role in spontaneous CNS autoimmunity, as neutralization of the cytokine in mice colonized with healthy-twin fecal samples increased disease incidence. These findings provide evidence that MS-derived microbiota contain factors that precipitate an MS-like autoimmune disease in a transgenic mouse model. They hence encourage the detailed search for protective and pathogenic microbial components in human MS.
In order to advance precision medicine, detailed clinical features ought to be described in a way that leverages current knowledge. Although data collected from biomedical research is expanding at an ...almost exponential rate, our ability to transform that information into patient care has not kept at pace. A major barrier preventing this transformation is that multi-dimensional data collection and analysis is usually carried out without much understanding of the underlying knowledge structure. Here, in an effort to bridge this gap, Electronic Health Records (EHRs) of individual patients are connected to a heterogeneous knowledge network called Scalable Precision Medicine Oriented Knowledge Engine (SPOKE). Then an unsupervised machine-learning algorithm creates Propagated SPOKE Entry Vectors (PSEVs) that encode the importance of each SPOKE node for any code in the EHRs. We argue that these results, alongside the natural integration of PSEVs into any EHR machine-learning platform, provide a key step toward precision medicine.
The ability to computationally predict whether a compound treats a disease would improve the economy and success rate of drug approval. This study describes Project Rephetio to systematically model ...drug efficacy based on 755 existing treatments. First, we constructed Hetionet (neo4j.het.io), an integrative network encoding knowledge from millions of biomedical studies. Hetionet v1.0 consists of 47,031 nodes of 11 types and 2,250,197 relationships of 24 types. Data were integrated from 29 public resources to connect compounds, diseases, genes, anatomies, pathways, biological processes, molecular functions, cellular components, pharmacologic classes, side effects, and symptoms. Next, we identified network patterns that distinguish treatments from non-treatments. Then, we predicted the probability of treatment for 209,168 compound-disease pairs (het.io/repurpose). Our predictions validated on two external sets of treatment and provided pharmacological insights on epilepsy, suggesting they will help prioritize drug repurposing candidates. This study was entirely open and received realtime feedback from 40 community members.
Monozygotic or 'identical' twins have been widely studied to dissect the relative contributions of genetics and environment in human diseases. In multiple sclerosis (MS), an autoimmune demyelinating ...disease and common cause of neurodegeneration and disability in young adults, disease discordance in monozygotic twins has been interpreted to indicate environmental importance in its pathogenesis. However, genetic and epigenetic differences between monozygotic twins have been described, challenging the accepted experimental model in disambiguating the effects of nature and nurture. Here we report the genome sequences of one MS-discordant monozygotic twin pair, and messenger RNA transcriptome and epigenome sequences of CD4(+) lymphocytes from three MS-discordant, monozygotic twin pairs. No reproducible differences were detected between co-twins among approximately 3.6 million single nucleotide polymorphisms (SNPs) or approximately 0.2 million insertion-deletion polymorphisms. Nor were any reproducible differences observed between siblings of the three twin pairs in HLA haplotypes, confirmed MS-susceptibility SNPs, copy number variations, mRNA and genomic SNP and insertion-deletion genotypes, or the expression of approximately 19,000 genes in CD4(+) T cells. Only 2 to 176 differences in the methylation of approximately 2 million CpG dinucleotides were detected between siblings of the three twin pairs, in contrast to approximately 800 methylation differences between T cells of unrelated individuals and several thousand differences between tissues or between normal and cancerous tissues. In the first systematic effort to estimate sequence variation among monozygotic co-twins, we did not find evidence for genetic, epigenetic or transcriptome differences that explained disease discordance. These are the first, to our knowledge, female, twin and autoimmune disease individual genome sequences reported.
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Dostopno za:
DOBA, IJS, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Modern tools for genetic analysis are producing a large impact on our understanding of autoimmunity. More than 30 genome-wide association studies (GWAS) have been published to date in several ...autoimmune diseases (AID) and hundreds of common variants have been identified that confer risk or protection. While statistical adjustments are essential to refine the list of potential associations with each disease, valuable information can be extracted by the systematic collection of moderately significant variants present in more than one trait. In this article, a compilation of all GWAS published to date in seven common AID is provided and a network-based analysis of shared susceptibility genes at different levels of significance is presented. While involvement of the MHC region in chromosome 6p21 is not in question for most AID, the complex genetic architecture of this locus poses a significant analytical challenge. On the other hand, by considering the contribution of non-MHC-related genes, similarities and differences among AID can be readily computed thus gaining insights into possible pathogenic mechanisms. Statistically significant excess sharing of non-MHC genes was found between type I diabetes (T1D) and all other AID studied, a result also seen for RA. A smaller but significant degree of sharing was observed for multiple sclerosis (MS), Celiac disease (CeD) and Crohn's disease (CD). The availability of GWAS data allows for a systematic analysis of similarities and differences among several AID. Using this class of approaches the unique genetic landscape for each autoimmune disease can start to be defined.
Multiple sclerosis (MS) is a prevalent inflammatory disease of the central nervous system that often leads to disability in young adults. Treatment options are limited and often only partly ...effective. The disease is likely caused by a complex interaction between multiple genes and environmental factors, leading to inflammatory‐mediated central nervous system deterioration. A series of genomic studies have confirmed a central role for the immune system in the development of MS, including genetic association studies that have now dramatically expanded the roster of MS susceptibility genes beyond the longstanding human leukocyte antigen (HLA) association in MS first identified nearly 40 years ago. Advances in technology together with novel models for collaboration across research groups have enabled the discovery of more than 50 non‐HLA genetic risk factors associated with MS. However, with a large proportion of the disease heritability still unaccounted for, current studies are now geared towards identification of causal alleles, associated pathways, epigenetic mechanisms, and gene–environment interactions. This article reviews recent efforts in addressing the genetics of MS and the challenges posed by an ever increasing amount of analyzable data, which is spearheading development of novel statistical methods necessary to cope with such complexity.
Differences in gene expression patterns have been documented not only in Multiple Sclerosis patients versus healthy controls but also in the relapse of the disease. Recently a new gene expression ...modulator has been identified: the microRNA or miRNA. The aim of this work is to analyze the possible role of miRNAs in multiple sclerosis, focusing on the relapse stage. We have analyzed the expression patterns of 364 miRNAs in PBMC obtained from multiple sclerosis patients in relapse status, in remission status and healthy controls. The expression patterns of the miRNAs with significantly different expression were validated in an independent set of samples. In order to determine the effect of the miRNAs, the expression of some predicted target genes of these were studied by qPCR. Gene interaction networks were constructed in order to obtain a co-expression and multivariate view of the experimental data. The data analysis and later validation reveal that two miRNAs (hsa-miR-18b and hsa-miR-599) may be relevant at the time of relapse and that another miRNA (hsa-miR-96) may be involved in remission. The genes targeted by hsa-miR-96 are involved in immunological pathways as Interleukin signaling and in other pathways as wnt signaling. This work highlights the importance of miRNA expression in the molecular mechanisms implicated in the disease. Moreover, the proposed involvement of these small molecules in multiple sclerosis opens up a new therapeutic approach to explore and highlight some candidate biomarker targets in MS.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Disruption of the blood-brain barrier (BBB) is critical to initiation and perpetuation of disease in multiple sclerosis (MS). We report an interaction between oligodendroglia and vasculature in MS ...that distinguishes human white matter injury from normal rodent demyelinating injury. We find perivascular clustering of oligodendrocyte precursor cells (OPCs) in certain active MS lesions, representing an inability to properly detach from vessels following perivascular migration. Perivascular OPCs can themselves disrupt the BBB, interfering with astrocyte endfeet and endothelial tight junction integrity, resulting in altered vascular permeability and an associated CNS inflammation. Aberrant Wnt tone in OPCs mediates their dysfunctional vascular detachment and also leads to OPC secretion of Wif1, which interferes with Wnt ligand function on endothelial tight junction integrity. Evidence for this defective oligodendroglial-vascular interaction in MS suggests that aberrant OPC perivascular migration not only impairs their lesion recruitment but can also act as a disease perpetuator via disruption of the BBB.
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