No abstract

Changes of intestinal permeability (IP) have been extensively investigated in inflammatory bowel diseases (IBD) and celiac disease (CD), underpinned by a known unbalance between microbiota, IP and immune responses in the gut. Recently the influence of IP on brain function has greatly been appreciated. Previous works showed an increased IP that preceded experimental autoimmune encephalomyelitis development and worsened during disease with disruption of TJ. Moreover, studying co-morbidity between Crohn's disease and MS, a report described increased IP in a minority of cases with MS. In a recent work we found that an alteration of IP is a relatively frequent event in relapsing-remitting MS, with a possible genetic influence on the determinants of IP changes (as inferable from data on twins); IP changes included a deficit of the active mechanism of absorption from intestinal lumen. The results led us to hypothesize that gut may contribute to the development of MS, as suggested by another previous work of our group: a population of CD8+CD161high T cells, belonging to the mucosal-associated invariant T (MAIT) cells, a gut- and liver-homing subset, proved to be of relevance for MS pathogenesis. We eventually suggest future lines of research on IP in MS: studies on IP changes in patients under first-line oral drugs may result useful to improve their therapeutic index; correlating IP and microbiota changes, or IP and blood-brain barrier changes may help clarify disease pathogenesis; exploiting the IP data to disclose co-morbidities in MS, especially with CD and IBD, may be important for patient care.

The gut barrier consists of several components, including the mucus layer, made of mucins and anti-bacterial molecule, the epithelial cells, connected by tight junction proteins, and a mixed population of cells involved in the interplay with microbes, such as M cells, elongations of “antigen presenting cells” dwelling the lamina propria, intraepithelial lymphocytes and Paneth cells secreting anti-bacterial peptides. Recently, the influence of intestinal permeability (IP) changes on organs far from gut has been investigated, and IP changes in multiple sclerosis (MS) have been described. A related topic is the microbiota dysfunction that underpins the development of neuroinflammation in animal models and human diseases, including MS. It becomes now of interest to better understand the mechanisms through which IP changes contribute to pathophysiology of neuroinflammation. The following aspects seem of relevance: studies on other biomarkers of IP alterations; the relationship with known risk factors for MS development, such as vitamin D deficiency; the link between blood brain barrier and gut barrier breakdown; the effects of IP increase on microbial translocation and microglial activation; the parallel patterns of IP and neuroimmune changes in MS and neuropsychiatric disorders, that afflict a sizable proportion of patients with MS. We will also discuss the therapeutic implications of IP changes, considering the impact of MS-modifying therapies on gut barrier, as well as potential approaches to enhance or protect IP homeostasis.

Background: Multiple sclerosis has an extremely variable natural course. In most patients, disease starts with a relapsing-remitting (RR) phase, which proceeds to a secondary progressive (SP) form. The duration of the RR phase is hard to predict, and to date predictions on the rate of disease progression remain suboptimal. This limits the opportunity to tailor therapy on an individual patient's prognosis, in spite of the choice of several therapeutic options. Approaches to improve clinical decisions, such as collective intelligence of human groups and machine learning algorithms are widely investigated. Methods: Medical students and a machine learning algorithm predicted the course of disease on the basis of randomly chosen clinical records of patients that attended at the Multiple Sclerosis service of Sant'Andrea hospital in Rome. Results: A significant improvement of predictive ability was obtained when predictions were combined with a weight that depends on the consistence of human (or algorithm) forecasts on a given clinical record. Conclusions: In this work we present proof-of-principle that human-machine hybrid predictions yield better prognoses than machine learning algorithms or groups of humans alone. To strengthen this preliminary result, we propose a crowdsourcing initiative to collect prognoses by physicians on an expanded set of patients.

The momentum of gene therapy in Huntington's disease (HD) deserves biomarkers from easily accessible fluid. We planned a study to verify whether plasma miRNome may provide useful peripheral “reporter(s)” for the management of HD patients. We performed an exploratory microarray study of whole non-coding RNA profiles in plasma from nine patients with HD and 13 matched controls [eight healthy subjects (HS) and five psychiatric patients (PP) to minimize possible iatrogenic impact on the profile of non-coding RNAs]. We found an HD-specific signature: downregulation of hsa-miR-98 (fold change, −1.5, p = 0.0338 HD vs. HS, and fold change, 1.5, p = 0.0045 HD vs. PP) and upregulation of hsa-miR-323b-3p (fold change, 1.5, p = 0.0007 HD vs. HS, and fold change, 1.5, p = 0.0111 HD vs. PP). To validate this result in an independent cohort, we quantify by digital droplet PCR (ddPCR) the presence of the two microRNA in the plasma of 33 HD patients and 49 matched controls (25 HS and 24 PP patients). We were able to confirm that hsa-miR-323b-3p was upregulated in HD and premanifest HD vs. HS and PP: the median values (first–third quartile) were 4.1 (0.9–10.53) and 5.8 (1.9–10.70) vs. 0.69 (0.3–2.75) and 1.4 (0.78–2.70), respectively, p < 0.05. No significant difference was found for hsa-miR-98. To evaluate the biological plausibility of the hsa-miR-323b-3p as a component of the disease pathophysiology, we performed a bioinformatic analysis based on its targetome and the huntingtin (HTT) interactome. We found a statistically significant overconnectivity between the targetome of hsa-miR-323b-3p and the HTT interactome (p = 1.48e−08). Furthermore, there was a significant transcription regulation of the HTT interactome by the miR-323b-3p targetome (p = 0.02). The availability of handy, reproducible, and minimally invasive biomarkers coming from peripheral miRNome may be valuable to characterize the illness progression, to indicate new therapeutic targets, and to monitor the effect of disease-modifying treatments. Our data deserve further studies with larger sample size and longitudinal design.

Background: Along with vitamin D, smoking, body mass index and others, Epstein Barr virus, other herpesviruses and human endogenous retroviruses represent plausible environmental risk factors for multiple sclerosis. However, it is difficult to obtain direct proof of their involvement in the etiology of this condition. Case presentation: In order to contribute further evidence of the importance of these viruses, and speculate about disease-relevant interactions between these agents and a predisposed genetic background of the host, we describe the temporal association between multiple sclerosis onset and Herpes simplex 1-encephalitis in a female patient. Conclusions: This case illustrates a possible relationship between HSV-1 encephalitis and multiple sclerosis. Bearing in mind that association does not imply causation, some speculations about the etiology and pathophysiology of the two diseases can be made. The hypothesis of a genetic background predisposing to HSV-1 encephalitis and to immune-mediated demyelination is supported by the coincidence of the two conditions in this patient, along with data from animal models and genetic studies.

The emerging concept of a crosstalk between hemostasis, inflammation, and immune system prompt recent works on coagulation cascade in multiple sclerosis (MS). Studies on MS pathology identified several coagulation factors since the beginning of the disease pathophysiology: fibrin deposition with breakdown of blood brain barrier, and coagulation factors within active plaques may exert pathogenic role, especially through the innate immune system. Studies on circulating coagulation factors showed complex imbalance involving several components of hemostasis cascade (thrombin, factor X, factor XII). To analyze the role of the coagulation process in connection with other pathogenic pathways, we implemented a systematic matching of genome-wide association studies (GWAS) data with an informative and unbiased network of coagulation pathways. Using MetaCore (version 6.35 build 69300, 2018) we analyzed the connectivity (i.e., direct and indirect interactions among two networks) between the network of the coagulation process and the network resulting from feeding into MetaCore the MS GWAS data. The two networks presented a remarkable over-connectivity: 958 connections vs. 561 expected by chance; z -score = 17.39; p -value < 0.00001. Moreover, genes coding for cluster of differentiation 40 (CD40) and plasminogen activator, urokinase (PLAU) shared both networks, pointed to an integral interplay between coagulation cascade and main pathogenic immune effectors. In fact, CD40 pathways is especially operative in B cells, that are currently a major therapeutic target in MS field. The potential interaction of PLAU with a signal of paramount importance for B cell pathogenicity, such as CD40, suggest new lines of research and pave the way to implement new therapeutic targets.

Background: Multiple sclerosis has an extremely variable natural course. In most patients, disease starts with a relapsing-remitting (RR) phase, which proceeds to a secondary progressive (SP) form. The duration of the RR phase is hard to predict, and to date predictions on the rate of disease progression remain suboptimal. This limits the opportunity to tailor therapy on an individual patient's prognosis, in spite of the choice of several therapeutic options. Approaches to improve clinical decisions, such as collective intelligence of human groups and machine learning algorithms are widely investigated. Methods: Medical students and a machine learning algorithm predicted the course of disease on the basis of randomly chosen clinical records of patients that attended at the Multiple Sclerosis service of Sant'Andrea hospital in Rome. Results: A significant improvement of predictive ability was obtained when predictions were combined with a weight that depends on the consistence of human (or algorithm) forecasts on a given clinical record. Conclusions: In this work we present proof-of-principle that human-machine hybrid predictions yield better prognoses than machine learning algorithms or groups of humans alone. To strengthen and generalize this preliminary result, we propose a crowdsourcing initiative to collect prognoses by physicians on an expanded set of patients.