Multiple sclerosis and HLA genotypes: A possible influence on brain atrophy

Lorefice, Lorena; Fenu, Giuseppe; Sardu, Claudia; Frau, Jessica; Coghe, Giancarlo; Costa, Gianna; Schirru, Lucia; Secci, Maria Antonietta; Sechi, Vincenzo; Barracciu, Maria Antonietta; Marrosu, Maria Giovanna; Cocco, Eleonora

doi:10.1177/1352458517739989

Cited by 14 publications

(13 citation statements)

References 35 publications

(49 reference statements)

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“…Unlike demyelination, water volume fluctuations and transient biological factors, neuroaxonal damage is irreversible in CNS, and atrophy is primarily considered to reflect this neurodegenerative component in MS [27][28][29][30]. Finally, the atrophy rates may also be influenced to some extent by the genetic makeup of a person; Human leukocyte antigen (HLA) genotypes considered as 'high risk for MS' , namely DRB1 and DQB1, have been associated with significantly lower WM and GM volumes, alongside with higher mean annualized percentage of brain volume change (PBVC) compared with medium and low risk HLA genotypes independent from patients clinical features (age, gender, disease course) or the DMTs used [31].…”

Section: Introductionmentioning

confidence: 99%

Brain atrophy in multiple sclerosis: mechanisms, clinical relevance and treatment options

Andravizou

Dardiotis

Artemiadis

et al. 2019

Autoimmun Highlights

114

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Multiple sclerosis (MS) is an immune-mediated disease of the central nervous system characterized by focal or diffuse inflammation, demyelination, axonal loss and neurodegeneration. Brain atrophy can be seen in the earliest stages of MS, progresses faster compared to healthy adults, and is a reliable predictor of future physical and cognitive disability. In addition, it is widely accepted to be a valid, sensitive and reproducible measure of neurodegeneration in MS. Reducing the rate of brain atrophy has only recently been incorporated as a critical endpoint into the clinical trials of new or emerging disease modifying drugs (DMDs) in MS. With the advent of easily accessible neuroimaging softwares along with the accumulating evidence, clinicians may be able to use brain atrophy measures in their everyday clinical practice to monitor disease course and response to DMDs. In this review, we will describe the different mechanisms contributing to brain atrophy, their clinical relevance on disease presentation and course and the effect of current or emergent DMDs on brain atrophy and neuroprotection. which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

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Section: Introductionmentioning

confidence: 99%

Brain atrophy in multiple sclerosis: mechanisms, clinical relevance and treatment options

Andravizou

Dardiotis

Artemiadis

et al. 2019

Autoimmun Highlights

114

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“…Most of the hub genes are located in HLA-DR loci in the MHC region, and the genes were found to be associated with immunity. Screening the functions of hub genes also con rms that the ten genes are susceptible genes for multiple sclerosis [20][21][22][23][24][25][26][27][28][29][30] . HLA-DRA, a member of the HLA class II alpha chain, is expressed on the surface of various antigen-presenting cells and plays a central role in the immune response through presenting polypeptides, particularly of pathogenic origin, to T cells.…”

Section: Discussionmentioning

confidence: 99%

Integration of Genetic and Immune Infiltration Insights into Data Mining of Multiple Sclerosis Pathogenesis

Zhang¹,

Song

Chen

et al. 2021

Preprint

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Background: Multiple sclerosis (MS) is an immune-mediated demyelinating disease of the central nervous system. MS pathogenesis is closely related to the environment, genetic, and immune system, but the underlying interactions have not been clearly elucidated. This study aims to unveil the genetic basis and immune landscape of MS pathogenesis with bioinformatics.Methods:Gene matrix wasretrieved from the gene expression database NCBI GEO. Then, bioinformatics was used to standardize the samples and obtain differentially expressed genes (DEGs). The protein-protein interaction network was constructed with DEGs on the STRING website. Cytohubbaplug-in and MCODE plug-in were used to mine hub genes. Meanwhile, the CIBERSORTX algorithm was used to explore the characteristics of immune cellinfiltration in MS brain tissues. Spearman correlation analysis was performed between genes and immune cells, and the correlation between genes and different types of brain tissues was also analyzed using the WGCNA method.Results:A total of 90 samples from 2 datasetswere included, and 882 DEGs and 10 hub genes closely related to MS were extracted. Functional enrichment analysis suggested the roleof immune response in MS. Besides,CIBERSORTX algorithm results showed that MS brain tissuescontained a variety of infiltrating immune cells. Correlation analysis suggested that the hub genes were highly relevant to chronic active white matter lesions.Certain hub genes played a role in the activation of immune cells such as macrophages and natural killer cells.Conclusions: Our study shall provideguidance for the further study of the genetic basis and immune infiltration mechanism of MS.

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“…Lorefice et al studied 240 MS patients and found that MS cases with high-risk HLA alleles tended to have more brain volume risk than lowrisk HLA groups. 33 In 2015, the IMSGC analyzed SNP data from 17 465 cases and 30 385 controls to impute classical HLA alleles. 34 Among class II and class I HLA alleles, they found evidence for two interactions involving pairs of class II alleles, HLA-DQA1*01:01.…”

Section: Mhc Locus and The Hla Imputation Methodsmentioning

confidence: 99%

“…Lorefice et al. studied 240 MS patients and found that MS cases with high‐risk HLA alleles tended to have more brain volume risk than low‐risk HLA groups . In 2015, the IMSGC analyzed SNP data from 17 465 cases and 30 385 controls to impute classical HLA alleles .…”

Section: Mhc Locus and The Hla Imputation Methodsmentioning

confidence: 99%

Statistical genetics and its application to neuroimmunology

Ogawa

Okada

2018

Clinical & Exp Neuroim

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Statistical genetics is a field involving the analysis of genetic data. With the development of next‐generation sequencing and single‐nucleotide polymorphism microarrays, the amount of genetic data is increasing rapidly. Genome‐wide association studies are one example of the successful application of an approach based on statistical genetics. To shed light on neuroinflammatory diseases, such as multiple sclerosis, >100 000 people have been enrolled in the genome‐wide association study meta‐analysis, and 200 loci associated with multiple sclerosis have been detected. To utilize these genetic data for clinical medicine, development of novel methods of statistical genetics is warranted. Human leukocyte antigen imputation and in silico drug repositioning are good examples of such approaches.

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Multiple sclerosis and HLA genotypes: A possible influence on brain atrophy

Abstract: Our results suggest an influence of HLA genotype on WB and GM atrophy. Further investigations are necessary to confirm these findings.

Cited by 14 publications

References 35 publications

Brain atrophy in multiple sclerosis: mechanisms, clinical relevance and treatment options

Brain atrophy in multiple sclerosis: mechanisms, clinical relevance and treatment options

Integration of Genetic and Immune Infiltration Insights into Data Mining of Multiple Sclerosis Pathogenesis

Statistical genetics and its application to neuroimmunology

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