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克拉屈滨和奥瑞珠单抗诱导复发缓解型多发性硬化患者外周血单个核细胞中差异的 miRNA 谱。

Cladribine and ocrelizumab induce differential miRNA profiles in peripheral blood mononucleated cells from relapsing-remitting multiple sclerosis patients.

机构信息

European Brain Research Institute (EBRI) Rita Levi-Montalcini, Rome, Italy.

Institute of Translational Pharmacology, National Research Council, Rome, Italy.

出版信息

Front Immunol. 2023 Dec 13;14:1234869. doi: 10.3389/fimmu.2023.1234869. eCollection 2023.

DOI:10.3389/fimmu.2023.1234869
PMID:38152407
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10751352/
Abstract

BACKGROUND AND OBJECTIVES

Multiple sclerosis (MS) is a chronic, progressive neurological disease characterized by early-stage neuroinflammation, neurodegeneration, and demyelination that involves a spectrum of heterogeneous clinical manifestations in terms of disease course and response to therapy. Even though several disease-modifying therapies (DMTs) are available to prevent MS-related brain damage-acting on the peripheral immune system with an indirect effect on MS lesions-individualizing therapy according to disease characteristics and prognostic factors is still an unmet need. Given that deregulated miRNAs have been proposed as diagnostic tools in neurodegenerative/neuroinflammatory diseases such as MS, we aimed to explore miRNA profiles as potential classifiers of the relapsing-remitting MS (RRMS) patients' prospects to gain a more effective DMT choice and achieve a preferential drug response.

METHODS

A total of 25 adult patients with RRMS were enrolled in a cohort study, according to the latest McDonald criteria before (pre-cladribine, pre-CLA; pre-ocrelizumab, pre-OCRE, time T0) and after high-efficacy DMTs, time T1, 6 months post-CLA ( = 10, 7 F and 3 M, age 39.0 ± 7.5) or post-OCRE ( = 15, 10 F and 5 M, age 40.5 ± 10.4) treatment. A total of 15 age- and sex-matched healthy control subjects (9 F and 6 M, age 36.3 ± 3.0) were also selected. By using Agilent microarrays, we analyzed miRNA profiles from peripheral blood mononuclear cells (PBMC). miRNA-target networks were obtained by miRTargetLink, and Pearson's correlation served to estimate the association between miRNAs and outcome clinical features.

RESULTS

First, the miRNA profiles of pre-CLA or pre-OCRE RRMS patients compared to healthy controls identified modulated miRNA patterns (40 and seven miRNAs, respectively). A direct comparison of the two pre-treatment groups at T0 and T1 revealed more pro-inflammatory patterns in the pre-CLA miRNA profiles. Moreover, both DMTs emerged as being capable of reverting some dysregulated miRNAs toward a protective phenotype. Both drug-dependent miRNA profiles and specific miRNAs, such as miR-199a-3p, miR-29b-3p, and miR-151a-3p, emerged as potentially involved in these drug-induced mechanisms. This enabled the selection of miRNAs correlated to clinical features and the related miRNA-mRNA network.

DISCUSSION

These data support the hypothesis of specific deregulated miRNAs as putative biomarkers in RRMS patients' stratification and DMT drug response.

摘要

背景与目的

多发性硬化症(MS)是一种慢性、进行性的神经系统疾病,其特征为早期神经炎症、神经退行性变和脱髓鞘,涉及疾病过程和治疗反应方面的一系列异质临床表现。尽管有几种疾病修正疗法(DMT)可用于预防与 MS 相关的脑损伤——通过对周围免疫系统的间接作用来影响 MS 病变——但根据疾病特征和预后因素进行个体化治疗仍然是一个未满足的需求。鉴于失调的 miRNA 已被提出作为 MS 等神经退行性/神经炎症性疾病的诊断工具,我们旨在探索 miRNA 谱作为复发性缓解型 MS(RRMS)患者的潜在分类器,以获得更有效的 DMT 选择并实现优先药物反应。

方法

根据最新的麦当劳标准,共纳入 25 例 RRMS 成年患者进行队列研究,分别在接受高效 DMT 之前(克拉屈滨前,CLA 前;奥瑞珠单抗前,OCRE 前,时间 T0)和之后,时间 T1,CLA 后 6 个月(=10 例,7 例女性和 3 例男性,年龄 39.0±7.5)或 OCRE 后(=15 例,10 例女性和 5 例男性,年龄 40.5±10.4)治疗。还选择了 15 名年龄和性别匹配的健康对照者(9 名女性和 6 名男性,年龄 36.3±3.0)。通过使用 Agilent 微阵列,我们分析了外周血单核细胞(PBMC)中的 miRNA 谱。通过 miRTargetLink 获得 miRNA 靶标网络,Pearson 相关用于估计 miRNA 与疾病特征之间的关联。

结果

首先,与健康对照组相比,CLA 前或 OCRE 前 RRMS 患者的 miRNA 谱确定了调节的 miRNA 模式(分别为 40 个和 7 个 miRNA)。在 T0 和 T1 时对这两个预处理组的直接比较显示,CLA 前 miRNA 谱中的促炎模式更多。此外,两种 DMT 都能够使一些失调的 miRNA 恢复为保护性表型。依赖药物的 miRNA 谱和特定的 miRNA,如 miR-199a-3p、miR-29b-3p 和 miR-151a-3p,被认为可能参与了这些药物诱导的机制。这使得能够选择与临床特征相关的 miRNA,并构建相关的 miRNA-mRNA 网络。

讨论

这些数据支持了特定失调的 miRNA 作为 RRMS 患者分层和 DMT 药物反应的潜在生物标志物的假设。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b9db/10751352/357042437d98/fimmu-14-1234869-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b9db/10751352/79949f427d59/fimmu-14-1234869-g001.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b9db/10751352/a0dce5e62088/fimmu-14-1234869-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b9db/10751352/7dbd322eee22/fimmu-14-1234869-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b9db/10751352/2e25993fca85/fimmu-14-1234869-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b9db/10751352/357042437d98/fimmu-14-1234869-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b9db/10751352/79949f427d59/fimmu-14-1234869-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b9db/10751352/6681108d2e31/fimmu-14-1234869-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b9db/10751352/a0dce5e62088/fimmu-14-1234869-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b9db/10751352/7dbd322eee22/fimmu-14-1234869-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b9db/10751352/2e25993fca85/fimmu-14-1234869-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b9db/10751352/357042437d98/fimmu-14-1234869-g006.jpg

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