The microRNA let-7b-5p Is Negatively Associated with Inflammation and Disease Severity in Multiple Sclerosis.
Ontology highlight
ABSTRACT:
SUBMITTER: Mandolesi G
PROVIDER: S-EPMC7915741 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:In order to assess potential alterations in microRNA expression in MS patients, we first applied microRNA array to screen the expression profile of 7 controls and 6 patients in the peripheral blood mononuclear cells (PBMC) PBMC when compared to age- and sex-matched controls Peripheral blood mononuclear cells (PBMC) were prepared by density gradient centrifugation using Nycoprep 1.077 medium (Accurate Chemical & Scientific Corp, Westbury, NY, USA). Total RNA including microRNAs were extracted using miRNeasy mini kit (Qiagen, Germantown, MD, USA) and subjected to microRNA high-throughput array with Affymetrix platform. MicroRNA array in Affymetrix 3.0 platform. Quantitative real-time PCR (QPCR) was applied for the validation and amplification of the test samples. Relative fold expression values were calculated based on the expression of Snord96A gene as an endogenous control. Microarray data are available in the ArrayExpress database (HUwww.ebi.ac.uk/arrayexpressUH) under accession number E-MEXP-4673.
2015-03-24 | E-MTAB-3437 | biostudies-arrayexpress
Project description:Objective: To test the hypothesis that Multiple Sclerosis (MS) patients have increased peripheral inflammation compared to healthy donors and that this systemic activation of the immune system, reflected by acute phase reactants (APRs) measured in the blood, contributes to intrathecal inflammation, which in turn contributes to the development of disability in MS. Methods: Eight serum APRs measured in a prospectively-collected cross-sectional cohort with a total of 51 healthy donors and 291 untreated MS patients were standardized and assembled into related biomarker clusters to derive global measures of systemic inflammation. The resulting APR clusters were compared between diagnostic categories and correlated to equivalently-derived cerebrospinal fluid (CSF) biomarkers of innate and adaptive immunity. Finally, correlations were calculated between biomarkers of systemic and intrathecal inflammation and MS severity measures, which predict future rates of disability progression. Results: While two blood APR clusters were elevated in MS patients, only one exhibited a weak correlation with MS severity. All CSF inflammation clusters, except CSF albumin, correlated with at least one measure of MS severity, with biomarkers of humoral adaptive immunity exhibiting the strongest correlations, especially in Progressive MS. Conclusion: Systemic inflammation does not appear to be strongly associated with intrathecal inflammation in MS. Positive correlations between markers of intrathecal inflammation, especially of humoral immunity, with MS severity measures support a pathogenic role of intrathecal (compartmentalized) inflammation in central nervous system tissue destruction, including in Progressive MS.
| S-EPMC6884093 | biostudies-literature
Project description:One of the classical features observed in patients with burn injury is hyperglycemia. There have been previous reports that a cohort of microRNAs (miRNAs) is differentially expressed in the dermis of patients with burn injury. More specifically, it has been shown that the miR-194 can target the insulin-like growth factor receptor 1 (IGF1R) and silence its protein expression resulting in hyperglycemia. The objective of the current study was to discover if additional miRNA-mediated post-transcriptional mechanism exists that lead to suppression of IGF1R protein expression post-burn injury. Using the 30% total body surface area (TBSA) model of burn injury in rats we found that the miRNA let-7b can target IGF1R and downregulate its protein expression, in turn attenuating PI3K/Akt and Gsk3β activation leading to hyperglycemia. Increased let-7b expression was significantly more than the previously reported miR-194 both in the burn rats compared to sham and in patients with burn injury compared to healthy subjects. Serum from burn rats also resulted in decreased IGF1R protein expression in rat L6 myotubes. In vivo targeting of let-7b by antagomir mitigated the effect of increased let-7b expression on IGF1R protein expression and hyperglycemia. Thus targeting let-7b might be a promising approach to treat hyperglycemia in patients with burn injury.
| S-EPMC5641176 | biostudies-literature
Project description:ObjectiveThis study was undertaken to assess the impact of immunosuppressive and immunomodulatory therapies on the severity of coronavirus disease 2019 (COVID-19) in people with multiple sclerosis (PwMS).MethodsWe retrospectively collected data of PwMS with suspected or confirmed COVID-19. All the patients had complete follow-up to death or recovery. Severe COVID-19 was defined by a 3-level variable: mild disease not requiring hospitalization versus pneumonia or hospitalization versus intensive care unit (ICU) admission or death. We evaluated baseline characteristics and MS therapies associated with severe COVID-19 by multivariate and propensity score (PS)-weighted ordinal logistic models. Sensitivity analyses were run to confirm the results.ResultsOf 844 PwMS with suspected (n = 565) or confirmed (n = 279) COVID-19, 13 (1.54%) died; 11 of them were in a progressive MS phase, and 8 were without any therapy. Thirty-eight (4.5%) were admitted to an ICU; 99 (11.7%) had radiologically documented pneumonia; 96 (11.4%) were hospitalized. After adjusting for region, age, sex, progressive MS course, Expanded Disability Status Scale, disease duration, body mass index, comorbidities, and recent methylprednisolone use, therapy with an anti-CD20 agent (ocrelizumab or rituximab) was significantly associated (odds ratio [OR] = 2.37, 95% confidence interval [CI] = 1.18-4.74, p = 0.015) with increased risk of severe COVID-19. Recent use (<1 month) of methylprednisolone was also associated with a worse outcome (OR = 5.24, 95% CI = 2.20-12.53, p = 0.001). Results were confirmed by the PS-weighted analysis and by all the sensitivity analyses.InterpretationThis study showed an acceptable level of safety of therapies with a broad array of mechanisms of action. However, some specific elements of risk emerged. These will need to be considered while the COVID-19 pandemic persists. ANN NEUROL 2021;89:780-789.
| S-EPMC8013440 | biostudies-literature
Project description:Multiple sclerosis (MS) is an autoimmune neuro-inflammatory disease arising from complex interactions of genetic, epigenetic, and environmental factors. Variations in genes of some microRNAs--key post-transcriptional regulators of many genes--can influence microRNAs expression/function and contribute to MS via expression changes of protein-coding target mRNA genes. We performed an association study of polymorphous variants of MIR146A rs2910164, MIR196A2 rs11614913, MIR499A rs3746444 MIR223 rs1044165 and their combinations with MS risk and severity. 561 unrelated patients with bout-onset MS and 441 healthy volunteers were enrolled in the study. We observed associations of MS risk with allele MIR223*T and combination (MIR223*T + MIR146A*G/G) carriage in the entire groups and in women at Bonferroni-corrected significance level (pcorr < 0.05). Besides, MIR146A*G/G association with MS was observed in women with nominal significance (pf = 0.025). No MS associations were found in men. A more severe MS course (MSSS value > 3.5) was associated with the carriage of MIR499A*C/T and, less reliably, of MIR499A*C (pcorr = 0.006 and pcorr = 0.024, respectively) and with the carriage of combinations (MIR499A*C/T + MIR196A2*C) and (MIR499A*C + MIR196A2*C) (pcorr = 0.00078 and pcorr = 0.0059, respectively). These associations also showed gender specificity, as they were not significant in men and substantially reinforced in women. The strongest association with MS severity was observed in women for combination (MIR499A*C/T + MIR196A2*C): pcorr = 4.43 × 10(-6) and OR = 3.23 (CI: 1.99-5.26).
| S-EPMC4581341 | biostudies-literature
Project description:Multiple Sclerosis (MS) is a chronic inflammatory demyelinating disease of the central nervous system (CNS). There is currently no single definitive test for MS. Circulating exosomes represent promising candidate biomarkers for a host of human diseases. Exosomes contain RNA, DNA, and proteins, can cross the blood-brain barrier, and are secreted from almost all cell types including cells of the CNS. We hypothesized that serum exosomal miRNAs could present a useful blood-based assay for MS disease detection and monitoring. Exosome-associated microRNAs in serum samples from MS patients (n?=?25) and matched healthy controls (n?=?11) were profiled using small RNA next generation sequencing. We identified differentially expressed exosomal miRNAs in both relapsing-remitting MS (RRMS) (miR-15b-5p, miR-451a, miR-30b-5p, miR-342-3p) and progressive MS patient sera (miR-127-3p, miR-370-3p, miR-409-3p, miR-432-5p) in relation to controls. Critically, we identified a group of nine miRNAs (miR-15b-5p, miR-23a-3p, miR-223-3p, miR-374a-5p, miR-30b-5p, miR-433-3p, miR-485-3p, miR-342-3p, miR-432-5p) that distinguished relapsing-remitting from progressive disease. Eight out of nine miRNAs were validated in an independent group (n?=?11) of progressive MS cases. This is the first demonstration that microRNAs associated with circulating exosomes are informative biomarkers not only for the diagnosis of MS, but in predicting disease subtype with a high degree of accuracy.
| S-EPMC5662562 | biostudies-literature
Project description:ObjectiveTo optimally leverage the scalability and unique features of the electronic health records (EHR) for research that would ultimately improve patient care, we need to accurately identify patients and extract clinically meaningful measures. Using multiple sclerosis (MS) as a proof of principle, we showcased how to leverage routinely collected EHR data to identify patients with a complex neurological disorder and derive an important surrogate measure of disease severity heretofore only available in research settings.MethodsIn a cross-sectional observational study, 5,495 MS patients were identified from the EHR systems of two major referral hospitals using an algorithm that includes codified and narrative information extracted using natural language processing. In the subset of patients who receive neurological care at a MS Center where disease measures have been collected, we used routinely collected EHR data to extract two aggregate indicators of MS severity of clinical relevance multiple sclerosis severity score (MSSS) and brain parenchymal fraction (BPF, a measure of whole brain volume).ResultsThe EHR algorithm that identifies MS patients has an area under the curve of 0.958, 83% sensitivity, 92% positive predictive value, and 89% negative predictive value when a 95% specificity threshold is used. The correlation between EHR-derived and true MSSS has a mean R(2) = 0.38±0.05, and that between EHR-derived and true BPF has a mean R(2) = 0.22±0.08. To illustrate its clinical relevance, derived MSSS captures the expected difference in disease severity between relapsing-remitting and progressive MS patients after adjusting for sex, age of symptom onset and disease duration (p = 1.56×10(-12)).ConclusionIncorporation of sophisticated codified and narrative EHR data accurately identifies MS patients and provides estimation of a well-accepted indicator of MS severity that is widely used in research settings but not part of the routine medical records. Similar approaches could be applied to other complex neurological disorders.
| S-EPMC3823928 | biostudies-literature
Project description:The search for the genetic foundation of multiple sclerosis (MS) severity remains elusive. It is, in fact, controversial whether MS severity is a stable feature that predicts future disability progression. If MS severity is not stable, it is unlikely that genotype decisively determines disability progression. An alternative explanation tested here is that the apparent instability of MS severity is caused by inaccuracies of its current measurement. We applied statistical learning techniques to a 902 patient-years longitudinal cohort of MS patients, divided into training (n = 133) and validation (n = 68) sub-cohorts, to test four hypotheses: (1) there is intra-individual stability in the rate of accumulation of MS-related disability, which is also influenced by extrinsic factors. (2) Previous results from observational studies are negatively affected by the insensitive nature of the Expanded Disability Status Scale (EDSS). The EDSS-based MS Severity Score (MSSS) is further disadvantaged by the inability to reliably measure MS onset and, consequently, disease duration (DD). (3) Replacing EDSS with a sensitive scale, i.e., Combinatorial Weight-Adjusted Disability Score (CombiWISE), and substituting age for DD will significantly improve predictions of future accumulation of disability. (4) Adjusting measured disability for the efficacy of administered therapies and other relevant external features will further strengthen predictions of future MS course. The result is a MS disease severity scale (MS-DSS) derived by conceptual advancements of MSSS and a statistical learning method called gradient boosting machines (GBM). MS-DSS greatly outperforms MSSS and the recently developed Age Related MS Severity Score in predicting future disability progression. In an independent validation cohort, MS-DSS measured at the first clinic visit correlated significantly with subsequent therapy-adjusted progression slopes (r = 0.5448, p = 1.56e-06) measured by CombiWISE. To facilitate widespread use of MS-DSS, we developed a free, interactive web application that calculates all aspects of MS-DSS and its contributing scales from user-provided raw data. MS-DSS represents a much-needed tool for genotype-phenotype correlations, for identifying biological processes that underlie MS progression, and for aiding therapeutic decisions.
| S-EPMC5686060 | biostudies-literature
Project description:BackgroundMultiple sclerosis patients would benefit from machine learning algorithms that integrates clinical, imaging and multimodal biomarkers to define the risk of disease activity.MethodsWe have analysed a prospective multi-centric cohort of 322 MS patients and 98 healthy controls from four MS centres, collecting disability scales at baseline and 2 years later. Imaging data included brain MRI and optical coherence tomography, and omics included genotyping, cytomics and phosphoproteomic data from peripheral blood mononuclear cells. Predictors of clinical outcomes were searched using Random Forest algorithms. Assessment of the algorithm performance was conducted in an independent prospective cohort of 271 MS patients from a single centre.ResultsWe found algorithms for predicting confirmed disability accumulation for the different scales, no evidence of disease activity (NEDA), onset of immunotherapy and the escalation from low- to high-efficacy therapy with intermediate to high-accuracy. This accuracy was achieved for most of the predictors using clinical data alone or in combination with imaging data. Still, in some cases, the addition of omics data slightly increased algorithm performance. Accuracies were comparable in both cohorts.ConclusionCombining clinical, imaging and omics data with machine learning helps identify MS patients at risk of disability worsening.
| S-EPMC10896787 | biostudies-literature
Project description:Recent association studies in multiple sclerosis (MS) have identified and replicated several single nucleotide polymorphism (SNP) susceptibility loci including CLEC16A, IL2RA, IL7R, RPL5, CD58, CD40 and chromosome 12q13-14 in addition to the well established allele HLA-DR15. There is potential that these genetic susceptibility factors could also modulate MS disease severity, as demonstrated previously for the MS risk allele HLA-DR15. We investigated this hypothesis in a cohort of 1006 well characterised MS patients from South-Eastern Australia. We tested the MS-associated SNPs for association with five measures of disease severity incorporating disability, age of onset, cognition and brain atrophy. We observed trends towards association between the RPL5 risk SNP and time between first demyelinating event and relapse, and between the CD40 risk SNP and symbol digit test score. No associations were significant after correction for multiple testing. We found no evidence for the hypothesis that these new MS disease risk-associated SNPs influence disease severity.
| S-EPMC2848851 | biostudies-literature