Altered Levels of mRNAs for Calcium-Binding/Associated Proteins, Annexin A1, S100A4, and TMEM64, in Peripheral Blood Mononuclear Cells Are Associated with Osteoporosis

Identification of hub genes associated with osteoporosis development by comprehensive bioinformatics analysis

Osteoporosis (OP) is a systemic bone disease caused by various factors, including, the decrease of bone density and quality, the destruction of bone microstructure, and the increase of bone fragility. It is a disease with a high incidence in a large proportion of the world's elderly population. However, osteoporosis lacks obvious symptoms and sensitive biomarkers. Therefore, it is extremely urgent to discover and identify disease-related biomarkers for early clinical diagnosis and effective intervention for osteoporosis. In our study, the Linear Models for Microarray Data (LIMMA) tool was used to screen differential expressed genes from transcriptome sequencing data of OP blood samples downloaded from the GEO database, and cluster Profiler was used for enriching analysis of differently expressed genes. In order to analyzed the relevance of gene modules, clinical symptoms, and the most related module setting genes associated with disease progression, we adapted Weighted Gene Co-expression Network Analysis (WGCNA) to screen and analyze the related pathways and relevant molecules. We used the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database to construct protein interaction network of key modules, and Cytoscape software was used to complete network visualization and screen of core genes in the network. Various plug-in algorithms of cytoHubba were used to identify key genes of OP. Finally, correlation analysis and single-gene gene probe concentration analysis (GSEA) analysis were performed for each core gene. Results of a total of 8 key genes that were closely related to the occurrence and development of OP were screened out, which provided a brand-new idea for the clinical diagnosis and early prevention of OP. Quantitative real-time PCR (qRT-PCR) was performed for validation, the expression levels of CUL1, PTEN and STAT1 genes in the OS group were significantly higher than in the non-OS groups. Receiver operating characteristic analysis demonstrated that CUL1, PTEN and STAT1 displayed considerable diagnostic accuracy for OS.

Annexins as potential targets in ocular diseases

Annexins (AnxAs) are Ca2+/phospholipid-binding proteins extensively studied and generally involved in several diseases. Although evidence exists regarding the distribuition of AnxAs in the visual system, their exact roles and the exact cell types of the eye where these proteins are expressed are not well-understood. AnxAs have pro-resolving roles in infectious, autoimmune, degenerative, fibrotic and angiogenic conditions, making them an important target in ocular tissue homeostasis. This review summarizes the current knowledge on the distribution and function of AnxA1-8 isoforms under normal and pathological conditions in the visual system, as well as perspectives for ophthalmologic treatments, including the potential use of the AnxA1 recombinant and/or its mimetic peptide Ac2-26.

A modified density gradient proteomic-based method to analyze endolysosomal proteins in cardiac tissue

The importance of lysosomes in cardiac physiology and pathology is well established, and evidence for roles in calcium signaling is emerging. We describe a label-free proteomics method suitable for small cardiac tissue biopsies based on density-separated fractionation, which allows study of endolysosomal (EL) proteins. Density gradient fractions corresponding to tissue lysate; sarcoplasmic reticulum (SR), mitochondria (Mito) (1.3 g/mL); and EL with negligible contamination from SR or Mito (1.04 g/mL) were analyzed using Western blot, enzyme activity assay, and liquid chromatography with tandem mass spectrometry (LC-MS/MS) analysis (adapted discontinuous Percoll and sucrose differential density gradient). Kyoto Encyclopedia of Genes and Genomes, Reactome, Panther, and Gene Ontology pathway analysis showed good coverage of RAB proteins and lysosomal cathepsins (including cardiac-specific cathepsin D) in the purified EL fraction. Significant EL proteins recovered included catalytic activity proteins. We thus present a comprehensive protocol and data set of guinea pig atrial EL organelle proteomics using techniques also applicable for non-cardiac tissue.

Machine learning analysis of gene expression profile reveals a novel diagnostic signature for osteoporosis

Background

Osteoporosis (OP) is increasingly prevalent with the aging of the world population. It is urgent to identify efficient diagnostic signatures for the clinical application.

Method

We downloaded the mRNA profile of 90 peripheral blood samples with or without OP from GEO database (Number: GSE152073). Weighted gene co-expression network analysis (WGCNA) was used to reveal the correlation among genes in all samples. GO term and KEGG pathway enrichment analysis was performed via the clusterProfiler R package. STRING database was applied to screen the interaction pairs among proteins. Protein–protein interaction (PPI) network was visualized based on Cytoscape, and the key genes were screened using the cytoHubba plug-in. The diagnostic model based on these key genes was constructed, and 5-fold cross validation method was applied to evaluate its reliability.

Results

A gene module consisted of 176 genes predicted to be associated with the occurrence of OP was identified. A total of 16 significantly enriched GO terms and 1 significantly enriched KEGG pathway were obtained based on the 176 genes. The top 50 key genes in the PPI network were identified. Then 22 genes were screened based on stepwise regression analysis from the 50 key genes. Of which, 9 genes were further screened out by multivariate regression analysis with the significant threshold of P value < 0.01. The diagnostic model was established based on the optimal 9 key genes, which efficiently separated the normal samples and OP samples.

Conclusion

A diagnostic model established based on nine key genes could reliably separate OP patients from healthy subjects, which provided novel lightings on the diagnostic research of OP.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13018-021-02329-1.