Shriwash Nitesh, Aiman Ayesha, Singh Prithvi, Basir Seemi Farhat, Shamsi Anas, Shahid Mohammad, Dohare Ravins, Islam Asimul
Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, Okhla, New Delhi, India.
Department of Biosciences, Faculty of Natural Sciences, Jamia Millia Islamia, Okhla, New Delhi, India.
PLoS One. 2024 Dec 19;19(12):e0314428. doi: 10.1371/journal.pone.0314428. eCollection 2024.
Multiple sclerosis (MS) is a complex neurological disorder marked by neuroinflammation and demyelination. Understanding its molecular basis is vital for developing effective treatments. This study aims to elucidate the molecular progression of MS using multiomics and network-based approach.
We procured differentially expressed genes in MS patients and healthy controls by accessing mRNA dataset from a publicly accessible database. The DEGs were subjected to a non-trait weighted gene co-expression network (WGCN) for hub DEGs identification. These hub DEGs were utilized for enrichment, protein-protein interaction network (PPIN), and feed-forward loop (FFL) analyses.
We identified 880 MS-associated DEGs. WGCN revealed a total of 122 hub DEGs of which most significant pathway, gene ontology (GO)-biological process (BP), GO-molecular function (MF) and GO-cellular compartment (CC) terms were assembly and cell surface presentation of N-methyl-D-aspartate (NMDA) receptors, regulation of catabolic process, NAD(P)H oxidase H2O2 forming activity, postsynaptic recycling endosome. The intersection of top 10 significant pathways, GO-BP, GO-MF, GO-CC terms, and PPIN top cluster genests identified STAT3 and CREB1 as key biomarkers. Based on essential centrality measures, CREB1 was retained as the final biomarker. Highest-order subnetwork FFL motif comprised one TF (KLF7), one miRNA (miR-328-3p), and one mRNA (CREB1) based on essential centrality measures.
This study provides insights into the roles of potential biomarkers in MS progression and offers a system-level view of its molecular landscape. Further experimental validation is needed to confirm these biomarkers' significance, which will lead to early diagnostic and therapeutic advancements.
多发性硬化症（MS）是一种以神经炎症和脱髓鞘为特征的复杂神经系统疾病。了解其分子基础对于开发有效的治疗方法至关重要。本研究旨在使用多组学和基于网络的方法阐明MS的分子进展。
我们通过访问一个可公开获取的数据库中的mRNA数据集，获取了MS患者和健康对照中差异表达的基因。将这些差异表达基因用于非特征加权基因共表达网络（WGCN）以识别枢纽差异表达基因。这些枢纽差异表达基因用于富集分析、蛋白质-蛋白质相互作用网络（PPIN）分析和前馈环（FFL）分析。
我们鉴定出880个与MS相关的差异表达基因。WGCN共揭示了122个枢纽差异表达基因，其中最显著的通路、基因本体（GO）-生物学过程（BP）、GO-分子功能（MF）和GO-细胞区室（CC）术语是N-甲基-D-天冬氨酸（NMDA）受体的组装和细胞表面呈递、分解代谢过程的调节、NAD（P）H氧化酶H2O2形成活性、突触后回收内体。前10个显著通路、GO-BP、GO-MF、GO-CC术语和PPIN顶级聚类基因的交集确定STAT3和CREB-1为关键生物标志物。基于基本中心性度量，CREB-1被保留为最终生物标志物。基于基本中心性度量，最高阶子网FFL模体由一个转录因子（KLF7）、一个微小RNA（miR-328-3p）和一个信使核糖核酸（CREB-1）组成。
本研究深入了解了潜在生物标志物在MS进展中的作用，并提供了其分子景观的系统层面视图。需要进一步的实验验证来确认这些生物标志物的重要性，这将推动早期诊断和治疗的进展。
