Explore the vast range of titles available.

During the past decade, research has revealed that the vast community of micro-organisms that inhabit the gut — known as the gut microbiota — is intricately linked to human health and disease, partly as a result of its influence on systemic immune responses. Accumulating evidence demonstrates that these effects on immune function are important in neuroinflammatory diseases, such as multiple sclerosis (MS), and that modulation of the microbiome could be therapeutically beneficial in these conditions. In this Review, we examine the influence that the gut microbiota have on immune function via modulation of serotonin production in the gut and through complex interactions with components of the immune system, such as T cells and B cells. We then present evidence from studies in mice and humans that these effects of the gut microbiota on the immune system are important in the development and course of MS. We also consider how strategies for manipulating the composition of the gut microbiota could be used to influence disease-related immune dysfunction and form the basis of a new class of therapeutics. The strategies discussed include the use of probiotics, supplementation with bacterial metabolites, transplantation of faecal matter or defined microbial communities, and dietary intervention. Carefully designed studies with large human cohorts will be required to gain a full understanding of the microbiome changes involved in MS and to develop therapeutic strategies that target these changes.In this Review, the authors provide detailed insight into how the gut microbiota influences the immune system, with implications for neuroinflammation, and discuss the accumulating evidence that the gut microbiota is an important factor in multiple sclerosis pathogenesis and a potential therapeutic target.

SignificanceStudies using experimental models have indicated that multiple sclerosis (MS)-like disease can be triggered in the gut following interactions of brain autoimmune T lymphocytes with local microbiota. Here we studied the gut microbiota from monozygotic human twin pairs discordant for multiple sclerosis. When we transferred human-derived microbiota into transgenic mice expressing a myelin autoantigen-specific T cell receptor, we found that gut microbiota from multiple sclerosis-affected twins induced CNS-specific autoimmunity at a higher incidence than microbiota from healthy co-twins. Our results offer functional evidence that human microbiome components contribute to CNS-specific autoimmunity. 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 twin-derived 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.

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.

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 Scalable Precision Medicine Oriented Knowledge Engine (SPOKE) is a heterogeneous knowledge network that integrates information from 29 public databases. Here, Nelson et al. extend SPOKE to embed clinical data from electronic health records to create medically meaningful barcodes for each medical variable.

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. Of all the data in the world today, 90% was created in the last two years. However, taking advantage of this data in order to advance our knowledge is restricted by how quickly we can access it and analyze it in a proper context. In biomedical research, data is largely fragmented and stored in databases that typically do not “talk” to each other, thus hampering progress. One particular problem in medicine today is that the process of making a new therapeutic drug from scratch is incredibly expensive and inefficient, making it a risky business. Given the low success rate in drug discovery, there is an economic incentive in trying to repurpose an existing drug that has already been shown to be safe and effective towards a new disease or condition. Himmelstein et al. used a computational approach to analyze 50,000 data points – including drugs, diseases, genes and symptoms – from 19 different public databases. This approach made it possible to create more than two million relationships among the data points, which could be used to develop models that predict which drugs currently in use by doctors might be best suited to treat any of 136 common diseases. For example, Himmelstein et al. identified specific drugs currently used to treat depression and alcoholism that could be repurposed to treat smoking addition and epilepsy. These findings provide a new and powerful way to study drug repurposing. While this work was exclusively performed with public data, an expanded and potentially stronger set of predictions could be obtained if data owned by pharmaceutical companies were incorporated. Additional studies will be needed to test the predictions made by the models.

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.The authors report aberrant oligodendrocyte precursor cell (OPC) interactions with blood vessels in certain multiple sclerosis lesions. These clustered OPCs can disrupt the blood–brain barrier and can impair OPC recruitment to repairing lesions.

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.

The genomics of multiple sclerosis Identical (or more correctly 'monozygotic') twins are widely used to study the contributions of genetics and environment to human disease. A study that focused on three pairs of monozygotic twins, in which one twin had multiple sclerosis and the other did not, has brought the latest techniques of genome sequencing and analysis to this field, and incidentally published the first female human genome sequences. Full sequences were determined for one pair of twins, and for these and the other two pairs the mRNA transcriptome and epigenome sequences of CD4 + lymphocytes were determined. The striking result is that no genetic, epigenetic or transcriptome differences were found that explained why one twin had the disease and the other did not. Digging deeper into the data, eQTL (expression quantitative trait locus) mapping revealed tantalizing differences within twin pairs that merit closer examination. And some possible causes can be ruled out. Future work might usefully concentrate on studies of other cell types and epigenetic modifications. Studies of identical twins are widely used to dissect the contributions of genes and the environment to human diseases. In multiple sclerosis, an autoimmune demyelinating disease, identical twins often show differences. This might suggest that environmental effects are most significant in this case, but genetic and epigenetic differences between identical twins have been described. Here, however, studies of identical twins show no evidence for genetic, epigenetic or transcriptome differences that could explain disease discordance. 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 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 . However, genetic and epigenetic differences between monozygotic twins have been described, challenging the accepted experimental model in disambiguating the effects of nature and nurture 9 , 10 , 11 , 12 . 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 ∼3.6 million single nucleotide polymorphisms (SNPs) or ∼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 ∼19,000 genes in CD4 + T cells. Only 2 to 176 differences in the methylation of ∼2 million CpG dinucleotides were detected between siblings of the three twin pairs, in contrast to ∼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.

Permanent damage to white matter tracts, comprising axons and myelinating oligodendrocytes, is an important component of brain injuries of the newborn that cause cerebral palsy and cognitive disabilities, as well as multiple sclerosis in adults. However, regulatory factors relevant in human developmental myelin disorders and in myelin regeneration are unclear. We found that AXIN2 was expressed in immature oligodendrocyte progenitor cells (OLPs) in white matter lesions of human newborns with neonatal hypoxic-ischemic and gliotic brain damage, as well as in active multiple sclerosis lesions in adults. Axin2 is a target of Wnt transcriptional activation that negatively feeds back on the pathway, promoting β-catenin degradation. We found that Axin2 function was essential for normal kinetics of remyelination. The small molecule inhibitor XAV939, which targets the enzymatic activity of tankyrase, acted to stabilize Axin2 levels in OLPs from brain and spinal cord and accelerated their differentiation and myelination after hypoxic and demyelinating injury. Together, these findings indicate that Axin2 is an essential regulator of remyelination and that it might serve as a pharmacological checkpoint in this process.

The multifaceted nature of multiple sclerosis requires quantitative biomarkers that can provide insights related to diverse physiological pathways. To this end, proteomic analysis of deeply-phenotyped serum samples, biological pathway modeling, and network analysis were performed to elucidate inflammatory and neurodegenerative processes, identifying sensitive biomarkers of multiple sclerosis disease activity. Here, we evaluated the concentrations of > 1400 serum proteins in 630 samples from three multiple sclerosis cohorts for association with clinical and radiographic new disease activity. Twenty proteins were associated with increased clinical and radiographic multiple sclerosis disease activity for inclusion in a custom assay panel. Serum neurofilament light chain showed the strongest univariate correlation with gadolinium lesion activity, clinical relapse status, and annualized relapse rate. Multivariate modeling outperformed univariate for all endpoints. A comprehensive biomarker panel including the twenty proteins identified in this study could serve to characterize disease activity for a patient with multiple sclerosis. Inflammatory and degenerative processes are thought to play a role in the pathophysiology of multiple sclerosis. Here, the authors identified twenty serum proteins associated with increased clinical and radiographic disease activity.