Background: Progressive multiple sclerosis (PMS) is an uncommon and severe subtype of MS that worsens gradually and leads to irreversible disabilities in young adults. Currently, there are no applicable or reliable biomarkers to distinguish PMS from relapsing–remitting multiple sclerosis (RRMS). Previous studies have demonstrated that dysfunction of N6-methyladenosine (m6A) RNA modification is relevant to many neurological disorders. Thus, the aim of this study was to explore the diagnostic biomarkers for PMS based on m6A regulatory genes in the cerebrospinal fluid (CSF). Methods: Gene expression matrices were downloaded from the ArrayExpress database. Then, we identified differentially expressed m6A regulatory genes between MS and non-MS patients. MS clusters were identified by consensus clustering analysis. Next, we analyzed the correlation between clusters and clinical characteristics. The random forest (RF) algorithm was applied to select key m6A-related genes. The support vector machine (SVM) was then used to construct a diagnostic gene signature. Receiver operating characteristic (ROC) curves were plotted to evaluate the accuracy of the diagnostic model. In addition, CSF samples from MS and non-MS patients were collected and used for external validation, as evaluated by an m6A RNA Methylation Quantification Kit and by real-time quantitative polymerase chain reaction. Results: The 13 central m6A RNA methylation regulators were all upregulated in MS patients when compared with non-MS patients. Consensus clustering analysis identified two clusters, both of which were significantly associated with MS subtypes. Next, we divided 61 MS patients into a training set (n = 41) and a test set (n = 20). The RF algorithm identified eight feature genes, and the SVM method was successfully applied to construct a diagnostic model. ROC curves revealed good performance. Finally, the analysis of 11 CSF samples demonstrated that RRMS samples exhibited significantly higher levels of m6A RNA methylation and higher gene expression levels of m6A-related genes than PMS samples. Conclusions: The dynamic modification of m6A RNA methylation is involved in the progression of MS and could potentially represent a novel CSF biomarker for diagnosing MS and distinguishing PMS from RRMS in the early stages of the disease.

Although familial forms of cerebral cavernous malformation are mainly attributed to three CCM genes (KRIT1, CCM2 and PDCD10), no mutation is identified in sporadic cerebral cavernous malformation cases with a unique lesion, indicating additional genes for sporadic cerebral cavernous malformation. To screen the candidate genes, we conducted whole exome sequencing in 31 sporadic cerebral cavernous malformation patients and 32 healthy controls, and identified 5 affected individuals carrying 6 heterozygous deleterious mutations in RNF213 but no RNF213 mutation in healthy individuals. To further confirm RNF213 was associated with cerebral cavernous malformation, we generated rnf213a homozygous knockout zebrafish and found mutation of rnf213a in zebrafish led to a mulberry-like cluster of disordered-flow vascular channels which was reminiscent of human cerebral cavernous malformation. In addition, we revealed kbtbd7 and anxa6 were significantly downregulated due to rnf213a mutation through transcriptomic sequencing and RT-qPCR analysis. Based on the mulberry-like phenotype partly rescued by mRNA of kbtbd7 as well as anxa6, we suggested that rnf213a promoted mulberry-like cluster via downregulation of kbtbd7 and anxa6. Altogether, we firstly demonstrate RNF213is a novel candidate gene for sporadic cerebral cavernous malformation and the mutation of rnf213a is responsible for the mulberry-like cluster in zebrafish.