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Integrated proteome sequencing, bulk RNA sequencing and single-cell RNA sequencing to identify potential biomarkers in different grades of intervertebral disc degeneration.

ABSTRACT: Low back pain (LBP) is a prevalent health problem worldwide that affects over 80% of adults during their lifetime. Intervertebral disc degeneration (IDD) is a well-recognized leading cause of LBP. IDD is classified into five grades according to the Pfirrmann classification system. The purpose of this study was to identify potential biomarkers in different IDD grades through an integrated analysis of proteome sequencing (PRO-seq), bulk RNA sequencing (bRNA-seq) and single-cell RNA sequencing (scRNA-seq) data. Eight cases of grade I-IV IDD were obtained. Grades I and II were considered non-degenerative discs (relatively normal), whereas grades III and IV were considered degenerative discs. PRO-seq analysis was performed to identify differentially expressed proteins (DEPs) in various IDD grades. Variation analysis was performed on bRNA-seq data to differentiate expressed genes (DEGs) in normal and degenerated discs. In addition, scRNA-seq was performed to validate DEGs in degenerated and non-degenerated nucleus pulposus (NP). Machine learning (ML) algorithms were used to screen hub genes. The receiver operating characteristic (ROC) curve was used to validate the efficiency of the screened hub genes to predict IDD. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to analyze function enrichment and signaling pathways. Protein-protein interaction (PPI) network was used to prioritize disease-related proteins. SERPINA1, ORM2, FGG and COL1A1 were identified through PRO-seq as the hub proteins involved in regulating IDD. ML algorithms selected ten hub genes, including IBSP, COL6A2, MMP2, SERPINA1, ACAN, FBLN7, LAMB2, TTLL7, COL9A3, and THBS4 in bRNA-seq. Since serine protease inhibitor clade A member 1 (SERPINA1) was the only common gene, its accuracy in degenerated and non-degenerated NP cells was validated using scRNA-seq. Then, the rat degeneration model of caudal vertebra was established. The expression of SERPINA1 and ORM2 was detected using immunohistochemical staining of human and rat intervertebral discs. The results showed that SERPINA1 was poorly expressed in the degenerative group. We further explored the potential function of SERPINA1 by Gene Set Enrichment Analysis (GSEA) and cell-cell communication. Therefore, SERPINA1 can be used as a biomarker to regulate or predict the progress of disc degeneration.

SUBMITTER: Yang X

PROVIDER: S-EPMC10061025 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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Integrated proteome sequencing, bulk RNA sequencing and single-cell RNA sequencing to identify potential biomarkers in different grades of intervertebral disc degeneration.

Yang Xiao X Lu Yang Y Zhou Hang H Jiang Hai-Tao HT Chu Lei L

Frontiers in cell and developmental biology 20230316

Low back pain (LBP) is a prevalent health problem worldwide that affects over 80% of adults during their lifetime. Intervertebral disc degeneration (IDD) is a well-recognized leading cause of LBP. IDD is classified into five grades according to the Pfirrmann classification system. The purpose of this study was to identify potential biomarkers in different IDD grades through an integrated analysis of proteome sequencing (PRO-seq), bulk RNA sequencing (bRNA-seq) and single-cell RNA sequencing (scR ...[more]

PMID: 37009470

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Project description:BackgroundNucleus pulposus (NP) deterioration plays a significant role in the development of intervertebral disc degeneration (IVDD) and low back pain (LBP). This paper aims to identify potential genes within degenerated NP tissue and elucidate the pathogenesis of IVDD through bioinformatics analysis.MethodsWe conducted a transcriptomic analysis of patient's degenerative NP tissue employing advanced bioinformatics techniques and machine learning algorithms. Utilizing hdWGCNA, we successfully acquired WGCNA single-cell sequencing data and pinpointed crucial genes implicated in IVDD. Subsequently, we employed the Monocle3 package to perform pseudotime sequence analysis, enabling the identification of genes associated with the differentiation and developmental processes of NP tissue. Following this, normalized and logarithmically transformed the bulk sequencing data. Subsequently, we conducted preliminary screening using single-factor logistic regression on the genes derived from single-cell sequencing. Next, we applied two machine learning techniques, namely, SVM-RFE and random forest, to discern pivotal pathogenic genes. Finally, we used validation sets to verify trends and qualitativeness and performed in vitro and in vivo validation analyses of normal and degenerative NP tissues.Results909 genes associated with IVDD were identified through hdWGCNA, while pseudotime sequence analysis uncovered 1964 genes related to differentiation and developmental processes. The two had 208 genes in common. Subsequently, we conducted an initial screening of single-cell genes by integrating the bulk database with single logistic regression. Next, we utilized machine learning techniques to identify the IVDD genes CDH, DPH5, and SELENOF. PCR analysis confirmed that the expression of CDH and DPH5 in degraded nucleus pulposus cells (NPCs) was decreased by 31% and 28% in vivo, and 36% and 29% in vitro, respectively, while SELENOF showed the opposite trend. Furthermore, IVDD was validated through imaging and histological staining.ConclusionAs pathogenic genes in IVDD, our findings indicate that CTH, DPH5, and SELENOF are important players and might be promising therapeutic targets for IVDD treatment.

Project description:BackgroundDegeneration of intervertebral disc is a major cause of lower back pain and neck pain. Studies have tried to unveil the regulatory network using either transcriptomic or proteomic analysis. However, neither have fully elucidated the exact mechanism of degeneration process. Since post-transcriptional regulation may affect gene expression by modulating the translational process of mRNA to protein product, a combined transcriptomic and proteomic study may provide more insight into the key regulatory network of Intervertebral disc degeneration.MethodsIn order to obtain the proteomic and transcriptomic data, we performed label-free proteome analysis on freshly isolated nucleus pulposus cells and obtained transcriptome profiling data from the Gene Expression Omnibus repository. To identify the key regulatory network of intervertebral disc degeneration in nucleus pulposus cells, we performed bioinformatic analyses and established a protein-RNA interacting network. To validate the candidate genes, we performed in vitro experimentation and immunochemistry labeling to identify their potential function during nucleus pulposus degeneration.ResultsThe label-free proteome analysis identified altogether 656 proteins, and 503 of which were differentially expressed between nucleus pulposus cells from degenerated or normal disc cells. Using the existing nucleus pulposus transcriptomic profiling data, we integrated the proteomic and transcriptomic data of nucleus pulposus cells, and established a protein-RNA interacting network to show the combined regulatory network of intervertebral disc degeneration. In the network, we found 9 genes showed significant changes, and 6 of which (CHI3L1, KRT19, COL6A2, DPT, TNFAIP6 and COL11A2) showed concordant changes in both protein and mRNA level. Further functional analysis showed these candidates can significantly affect the degeneration of the nucleus pulposus cell when altering their expression.ConclusionsThis study is the first to use combined analysis of proteomic and transcriptomic profiling data to identify novel regulatory network of nucleus pulposus cells in intervertebral disc degeneration. Our established protein-RNA interacting network demonstrated novel regulatory mechanisms and key genes that may play vital roles in the pathogenesis of intervertebral disc degeneration.

Dataset Information

Integrated proteome sequencing, bulk RNA sequencing and single-cell RNA sequencing to identify potential biomarkers in different grades of intervertebral disc degeneration.

Publications

Integrated proteome sequencing, bulk RNA sequencing and single-cell RNA sequencing to identify potential biomarkers in different grades of intervertebral disc degeneration.

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