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Multiple sclerosis and computational biology (Review).

ABSTRACT: Multiple sclerosis (MS) is an autoimmune neurodegenerative disease whose prevalence has increased worldwide. The resultant symptoms may be debilitating and can substantially reduce the of patients. Computational biology, which involves the use of computational tools to answer biomedical questions, may provide the basis for novel healthcare approaches in the context of MS. The rapid accumulation of health data, and the ever-increasing computational power and evolving technology have helped to modernize and refine MS research. From the discovery of novel biomarkers to the optimization of treatment and a number of quality-of-life enhancements for patients, computational biology methods and tools are shaping the field of MS diagnosis, management and treatment. The final goal in such a complex disease would be personalized medicine, i.e., providing healthcare services that are tailored to the individual patient, in accordance to the particular biology of their disease and the environmental factors to which they are subjected. The present review article summarizes the current knowledge on MS, modern computational biology and the impact of modern computational approaches of MS.

SUBMITTER: Diakou I

PROVIDER: S-EPMC9634047 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature

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Multiple sclerosis and computational biology (Review).

Diakou Io I Papakonstantinou Eleni E Papageorgiou Louis L Pierouli Katerina K Dragoumani Konstantina K Spandidos Demetrios A DA Bacopoulou Flora F Chrousos George P GP Goulielmos Georges Ν GΝ Eliopoulos Elias E Vlachakis Dimitrios D

Biomedical reports 20221018 6

Multiple sclerosis (MS) is an autoimmune neurodegenerative disease whose prevalence has increased worldwide. The resultant symptoms may be debilitating and can substantially reduce the of patients. Computational biology, which involves the use of computational tools to answer biomedical questions, may provide the basis for novel healthcare approaches in the context of MS. The rapid accumulation of health data, and the ever-increasing computational power and evolving technology have helped to mod ...[more]

PMID: 36382258

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Project description:Although fingolimod and interferon-β are two mechanistically different multiple sclerosis (MS) treatments, they both induce B cell activating factor (BAFF) and shift the B cell pool towards a regulatory phenotype. However, whether there is a shared mechanism between both treatments in how they influence the B cell compartment remains elusive. In this study, we collected a cross-sectional study population of 112 MS patients (41 untreated, 42 interferon-β, 29 fingolimod) and determined B cell subsets, cell-surface and RNA expression of BAFF-receptor (BAFF-R) and transmembrane activator and cyclophilin ligand interactor (TACI) as well as plasma and/or RNA levels of BAFF, BAFF splice forms and interleukin-10 (IL-10) and -35 (IL-35). We added an in vitro B cell culture with four stimulus conditions (Medium, CpG, BAFF and CpG+BAFF) for untreated and interferon-β treated patients including measurement of intracellular IL-10 levels. Our flow experiments showed that interferon-β and fingolimod induced BAFF protein and mRNA expression (P ≤ 3.15 x 10-4) without disproportional change in the antagonizing splice form. Protein BAFF correlated with an increase in transitional B cells (P = 5.70 x 10-6), decrease in switched B cells (P = 3.29 x 10-4), and reduction in B cell-surface BAFF-R expression (P = 2.70 x 10-10), both on TACI-positive and -negative cells. TACI and BAFF-R RNA levels remained unaltered. RNA, plasma and in vitro experiments demonstrated that BAFF was not associated with increased IL-10 and IL-35 levels. In conclusion, treatment-induced BAFF correlates with a shift towards transitional B cells which are enriched for cells with an immunoregulatory function. However, BAFF does not directly influence the expression of the immunoregulatory cytokines IL-10 and IL-35. Furthermore, the post-translational mechanism of BAFF-induced BAFF-R cell surface loss was TACI-independent. These observations put the failure of pharmaceutical anti-BAFF strategies in perspective and provide insights for targeted B cell therapies.

Project description:Background: Over the past two decades, various novel disease-modifying drugs for multiple sclerosis (MS) have been approved. However, there is high variability in the patient response to the available medications, which is hypothesized to be partly attributed to genetics. Objectives: To conduct a systematic review of the current literature on the pharmacogenomics of MS therapy. Methods: A systematic literature search was conducted using PubMed/MEDLINE database searching for articles investigating a role of genetic variation in response to disease-modifying MS treatments, published in the English language up to October 9th, 2018. PRISMA guidelines for systematic reviews were applied. Studies were included if they investigated response or nonresponse to MS treatment defined as relapse rate, by expanded disability status scale score or based on magnetic resonance imaging. The following data were extracted: first author's last name, year of publication, PMID number, sample size, ethnicity of patients, method, genes, and polymorphisms tested, outcome, significant associations with corresponding P-values and confidence intervals, response criteria, and duration of the follow-up period. Results: Overall, 48 articles published up to October 2018, evaluating response to interferon-beta, glatiramer acetate, mitoxantrone, and natalizumab, met our inclusion criteria and were included in this review. Among those, we identified 42 (87.5%) candidate gene studies and 6 (12.5%) genome-wide association studies. Existing pharmacogenomic evidence is mainly based on the results of individual studies, or on results of multiple studies, which often lack consistency. In recent years, hypothesis-free approaches identified novel candidate genes that remain to be validated. Various study designs, including the definition of clinical response, duration of the follow-up period, and methodology as well as moderate sample sizes, likely contributed to discordances between studies. However, some of the significant associations were identified in the same genes, or in the genes involved in the same biological pathways. Conclusions: At the moment, there is no available clinically actionable pharmacogenomic biomarker that would enable more personalized treatment of MS. More large-scale studies with uniform design are needed to identify novel and validate existing pharmacogenomics findings. Furthermore, studies investigating associations between rare variants and treatment response in MS patients, using next-generation sequencing technologies are warranted.

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Multiple sclerosis and computational biology (Review).

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Multiple sclerosis and computational biology (Review).

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