Sak Müge, Chariker Julia H, Rouchka Eric C
Kentucky IDeA Networks of Biomedical Research Excellence Data Science Core, Department of Neuroscience Training, University of Louisville, Louisville, KY 40292, USA.
Kentucky IDeA Networks of Biomedical Research Excellence Data Science Core, Department of Biochemistry and Molecular Genetics, University of Louisville, Louisville, KY 40292, USA.
Int J Mol Sci. 2025 Aug 23;26(17):8195. doi: 10.3390/ijms26178195.
Multiple sclerosis (MS) is an autoimmune and neurodegenerative disease affecting approximately 1 million people in the United States. Despite extensive research into the mechanisms of disease development, many aspects of the biological changes during MS progression and the varying symptoms among patients remain unclear. In the era of high-throughput sequencing, transcriptome databases are flooded with data. However, bulk RNA sequencing (RNA-seq) data are typically used only for differential gene expression analysis. Alternative splicing, a key process that alters the transcriptome, can also be identified from bulk data. Here, we accessed 11 studies with bulk RNA-seq data of postmortem MS patients' brain samples via NCBI's Gene Expression Omnibus (GEO). We extracted additional information from these data by identifying exclusively alternatively spliced genes via replicate multivariate analysis of transcript splicing (rMATS) analysis. Our analyses revealed that changes in RNA splicing mediate distinct biological signals compared to those driven by differential gene expression. Gene ontology and protein do-main analyses of genes exclusively regulated by alternative splicing revealed distinct molecular differences between progressive and relapsing-remitting MS as well as among lesions from different brain regions and between white and gray matter. These findings highlight the critical role of alternative splicing and its associated pathways in MS disease development and progression.
多发性硬化症（MS）是一种自身免疫性神经退行性疾病，在美国约有100万人受其影响。尽管对疾病发展机制进行了广泛研究，但MS进展过程中生物变化的许多方面以及患者之间不同的症状仍不清楚。在高通量测序时代，转录组数据库充斥着数据。然而，批量RNA测序（RNA-seq）数据通常仅用于差异基因表达分析。可变剪接是改变转录组的关键过程，也可以从批量数据中识别出来。在这里，我们通过NCBI的基因表达综合数据库（GEO）访问了11项关于MS患者死后脑样本的批量RNA-seq数据的研究。我们通过转录本剪接重复多变量分析（rMATS）分析专门识别可变剪接基因，从这些数据中提取了额外信息。我们的分析表明，与差异基因表达驱动的信号相比，RNA剪接变化介导了不同的生物信号。对由可变剪接专门调控的基因进行基因本体和蛋白质结构域分析，揭示了进行性和复发缓解型MS之间、不同脑区病变之间以及白质和灰质之间明显的分子差异。这些发现突出了可变剪接及其相关途径在MS疾病发展和进展中的关键作用。
