Familial multiple sclerosis in a Brazilian sample: Is HLA-DR15 involved in susceptibility to the disease?

Bernardes, Melina Da Silva; Paiva, Cláudia N.; Paradela, Eduardo Ribeiro; Alvarenga, Marcos Papais; Pereira, Fernanda; Vasconcelos, Cláudia Cristina Ferreira; Alvarenga, Regina Maria Papais

doi:10.1016/j.jneuroim.2019.02.004

Cited by 7 publications

(4 citation statements)

References 35 publications

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“…MS is the cause of death in more than 50% of the affected patients (122). Factors involved in the pathogenesis are both environmental and genetic (123)(124)(125)(126)(127)(128)(129)(130)(131). Among the environmental factors, microbes (and their secreted products or toxins) play a critical role in the pathogenesis of MS (132,133).…”

Section: Multiple Sclerosismentioning

confidence: 99%

The Gut-Brain Axis: How Microbiota and Host Inflammasome Influence Brain Physiology and Pathology

2020

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The human microbiota has a fundamental role in host physiology and pathology. Gut microbial alteration, also known as dysbiosis, is a condition associated not only with gastrointestinal disorders but also with diseases affecting other distal organs. Recently it became evident that the intestinal bacteria can affect the central nervous system (CNS) physiology and inflammation. The nervous system and the gastrointestinal tract are communicating through a bidirectional network of signaling pathways called the gut-brain axis, which consists of multiple connections, including the vagus nerve, the immune system, and bacterial metabolites and products. During dysbiosis, these pathways are dysregulated and associated with altered permeability of the blood-brain barrier (BBB) and neuroinflammation. However, numerous mechanisms behind the impact of the gut microbiota in neuro-development and -pathogenesis remain poorly understood. There are several immune pathways involved in CNS homeostasis and inflammation. Among those, the inflammasome pathway has been linked to neuroinflammatory conditions such as multiple sclerosis, Alzheimer’s and Parkinson’s diseases, but also anxiety and depressive-like disorders. The inflammasome complex assembles upon cell activation due to exposure to microbes, danger signals, or stress and lead to the production of pro-inflammatory cytokines (interleukin-1β and interleukin-18) and to pyroptosis. Evidences suggest that there is a reciprocal influence of microbiota and inflammasome activation in the brain. However, how this influence is precisely working is yet to be discovered. Herein, we discuss the status of the knowledge and the open questions in the field focusing on the function of intestinal microbial metabolites or products on CNS cells during healthy and inflammatory conditions, such as multiple sclerosis, Alzheimer’s and Parkinson’s diseases, and also neuropsychiatric disorders. In particular, we focus on the innate inflammasome pathway as immune mechanism that can be involved in several of these conditions, upon exposure to certain microbes.

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Section: Multiple Sclerosismentioning

confidence: 99%

The Gut-Brain Axis: How Microbiota and Host Inflammasome Influence Brain Physiology and Pathology

2020

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“…Through the GPL6947 platform (Illumina HumanHT-12 V3.0 expression beadchip), the mRNA expression profiling of 74 genes were acquired from 144 individuals, 99 with MS (43 PPMS, 36 RRMS, and 20 PSMS) and 45 healthy controls. The complete dataset is composed of the HLA-DRB1 gene, because it has a deep link to the risk of MS [5], and 73 expression profiles were taken as a reference from the 78 MS-related genes identified by Chen et al [12], of which five were not considered. The expression summary values were analyzed by GEO2R, an interactive web tool that allows viewing a specific gene expression through the profile graph tab.…”

Section: Data Importmentioning

confidence: 99%

“…Although MS has a complex etiology, human leukocyte antigen (HLA) genes have been implicated in disease susceptibility for four decades. HLA-Class II alleles represent the more significant genetic contribution to MS risk, specifically within the DR15 haplotype: HLA-DRB1*15:01, is a common finding in MS populations, primarily those of Northern European descent [5].…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Approach for Predicting Multiple Sclerosis

et al. 2023

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This paper proposes a deep learning model based on an artificial neural network with a single hidden layer for predicting the diagnosis of multiple sclerosis. The hidden layer includes a regularization term that prevents overfitting and reduces the model complexity. The purposed learning model achieved higher prediction accuracy and lower loss than four conventional machine learning techniques. A dimensionality reduction method was used to select the most relevant features from 74 gene expression profiles for training the learning models. The analysis of variance test was performed to identify the statistical difference between the mean of the proposed model and the compared classifiers. The experimental results show the effectiveness of the proposed artificial neural network.

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“…Regarding genetic risk factors, numerous HLA class I and II alleles have been associated with the risk of MS. The HLA DRB1 * 1501 allele has been found to have the strongest correlation with MS, increasing the risk of developing MS by approximately threefold (14,15).…”

Section: Introductionmentioning

confidence: 99%

The Role of Parental Consanguinity and Familial Aggregation in Development of Multiple Sclerosis: A Case-control Study

Vaheb,

Panah,

Afshari-Safavi

et al. 2024

Preprint

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Background Several studies pointed out the importance of genetic risk factors such as parental consanguinity (PC) and familial multiple sclerosis (FMS) in the risk of MS. This study aimed to investigate the PC and FMS among people with MS (pwMS) in Isfahan, Iran. Methods This case-control study was conducted on pwMS from the MS clinic of Kashani Hospital, Isfahan, Iran, between September 2022 and September 2023. Healthy controls (HC) were also recruited from the MS clinic. Data on demographic and clinical characteristics and history of PC and FMS were collected from participants. The relationships between PC, FMS, and developing MS were assessed using multinomial logistic regression analysis. The Odds ratio (OR) with a 95% confidence interval (CI) was computed. Results A total number of 4264 pwMS and 400 HCs were included. The prevalence of PC and FMS among pwMS were 29.3% and 24%, respectively. Multinomial logistic regression adjusted for age and sex indicated that the odds of developing MS were significantly associated with a history of PC (OR = 3.03, 95%CI: 2.23 to 4.13, p < 0.001) and FMS (OR = 5.42, 95%CI: 3.51 to 8.38, p < 0.001). Conclusion PC and FMS can increase the risk of developing MS. PC and FMS should be considered along with other risk factors for developing MS. A comprehensive conclusion requires further research.

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Familial multiple sclerosis in a Brazilian sample: Is HLA-DR15 involved in susceptibility to the disease?

Cited by 7 publications

References 35 publications

The Gut-Brain Axis: How Microbiota and Host Inflammasome Influence Brain Physiology and Pathology

The Gut-Brain Axis: How Microbiota and Host Inflammasome Influence Brain Physiology and Pathology

A Deep Learning Approach for Predicting Multiple Sclerosis

The Role of Parental Consanguinity and Familial Aggregation in Development of Multiple Sclerosis: A Case-control Study

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