Disease markers · 2022
Identification of Synovial Fibroblast-Associated Neuropeptide Genes and m6A Factors in Rheumatoid Arthritis Using Single-Cell Analysis and Machine Learning.
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Xiao Jianwei, Cai Xu, Wang Rongsheng, Zhou Weijian, Ye Zhizhong
Аннотация
OBJECTIVES: Synovial fibroblasts (SFs) play an important role in the development and progression of rheumatoid arthritis (RA). However, the pathogenic mechanism of SFs remains unclear. The objective of this study was to investigate how neuropeptides and N6-methyladenosine (m6A) played an important role in the underlying pathogenic processes of SFs that contribute to the development of RA.
METHODS: Single-cell RNA sequencing data were examined using single-cell analysis and machine learning. SF subgroups were identified based on the clustering and annotation results of the single-cell analysis. Moreover, cell-cell communication was used to analyse neuropeptide-related receptor and ligand pairs on the surface of SF cell membranes. Machine learning was used to explore the m6A factors acting on these neuropeptide genes.
RESULTS: NPR3, GHR, BDKRB2, and CALCRL, four neuropeptide genes, were shown to be differently expressed among SF subgroups. Further investigation of receptor-ligand interactions found that NPR3 (in conjunction with NPPC, OSTN, NPPB, and NPPA) and GHR (in conjunction with GH1 and GH2) may have a role in SF interactions. As predicted by machine learning, IGFBP2 and METTL3 were identified as key factors regulating m6A of NPR3 and GHR. The expression levels and enrichment pathways of METTL3 and IGFBP2 were different among SF subgroups.
CONCLUSIONS: Single-cell analysis and machine learning efficiently identified neuropeptide genes and m6A factors that perform important regulatory functions in RA. Our strategy may provide a basis for future studies to identify pathogenic cell subpopulations and molecular mechanisms in RA and other diseases.
METHODS: Single-cell RNA sequencing data were examined using single-cell analysis and machine learning. SF subgroups were identified based on the clustering and annotation results of the single-cell analysis. Moreover, cell-cell communication was used to analyse neuropeptide-related receptor and ligand pairs on the surface of SF cell membranes. Machine learning was used to explore the m6A factors acting on these neuropeptide genes.
RESULTS: NPR3, GHR, BDKRB2, and CALCRL, four neuropeptide genes, were shown to be differently expressed among SF subgroups. Further investigation of receptor-ligand interactions found that NPR3 (in conjunction with NPPC, OSTN, NPPB, and NPPA) and GHR (in conjunction with GH1 and GH2) may have a role in SF interactions. As predicted by machine learning, IGFBP2 and METTL3 were identified as key factors regulating m6A of NPR3 and GHR. The expression levels and enrichment pathways of METTL3 and IGFBP2 were different among SF subgroups.
CONCLUSIONS: Single-cell analysis and machine learning efficiently identified neuropeptide genes and m6A factors that perform important regulatory functions in RA. Our strategy may provide a basis for future studies to identify pathogenic cell subpopulations and molecular mechanisms in RA and other diseases.