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Transcriptional profiling and multiple machine learning methods identified novel microglia biomarkers for Experimental Autoimmune Encephalomyelitis

ABSTRACT: Transcriptional profiling and multiple machine learning methods identified novel microglia biomarkers for Experimental Autoimmune Encephalomyelitis

PROVIDER: PRJNA1065282 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR27559310_1.fastq.gz	Fastqsanger.gz
	SRR27559310_2.fastq.gz	Fastqsanger.gz
	SRR27559311_1.fastq.gz	Fastqsanger.gz
	SRR27559311_2.fastq.gz	Fastqsanger.gz
	SRR27559312_1.fastq.gz	Fastqsanger.gz

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Project description:Background: Microglia plays complex and crucial roles in multiple sclerosis (MS). This study aimed to explore the biological significance of microglia-associated genes in experimental autoimmune encephalomyelitis (EAE) . Methods: Differentially expressed genes (DEGs) were screened with six machine learning (ML) methods, which were also utilized to validate the microglia-associated DEGs in three public databases. ceRNA and Protein–protein interaction (PPI) network analyses were utilized to identify the interaction of the 6 novel biomarkers with other molecules. Then, CIBERSORT and single-sample gene set enrichment analysis (ssGSEA) were employed to quantify the relative abundance of each immune cell infiltration, respectively. qRT-PCR was performed to test the expression of key DEGs in murine models. Results: A total of 247 DEmRNA, 499 DElncRNAs and 269 DEcircRNAs were identified. With screening strategy of five ML algorithms, 6 DEmRNAs were obtained including NGP, HIST1H2BJ, PBLD1, MBLN3, CD180 and F10. Then the 6 DEmRNAs were used as a multigene signature to construct models to differentiate EAE from normal microglia, and AUC value for each model was greater than 0.8. The diagnostic value of these 6 DEmRNAs were identified and further verified by qRT-PCR. Then, differential expression for five out of these 6 DEmRNAs, namely NGP, HIST1H2BJ, PBLD1, MBLN3, and F10 were confirmed. Using PPI analysis, DEmRNAs frequently interacting with transcription factors (TFs), potential drugs and RBPs were identified. With immune cell infiltration analyses, we found EAE microglia presented high levels of immune infiltration, especially Nature Killer (NK) cells and CD8+ T cells. We also reported circRNA (circRNA_00638) was predicted to bind to 76 RBPs. Conclusions: We identified and validated 6 novel microglia related genes and developed a multigene signature with ML methods to confirm their ability to accurately diagnose and characterize biological alterations in EAE microglia. The six key DEmRNAs might also be latent targets for immunoregulatory therapy.

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Transcriptional profiling and multiple machine learning methods identified novel microglia biomarkers for Experimental Autoimmune Encephalomyelitis

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