Jianpi Qingchang Bushen decoction improves inflammatory response and metabolic bone disorder in inflammatory bowel disease-induced bone loss

Multi-omics Analysis to Identify Key Immune Genes for Osteoporosis based on Machine Learning and Single-cell Analysis

OBJECTIVE

Osteoporosis is a severe bone disease with a complex pathogenesis involving various immune processes. With the in-depth understanding of bone immune mechanisms, discovering new therapeutic targets is crucial for the prevention and treatment of osteoporosis. This study aims to explore novel bone immune markers related to osteoporosis based on single-cell and transcriptome data, utilizing bioinformatics and machine learning methods, in order to provide novel strategies for the diagnosis and treatment of the disease.

METHODS

Single cell and transcriptome data sets were acquired from Gene Expression Omnibus (GEO). The data was then subjected to cell communication analysis, pseudotime analysis, and high dimensional WGCNA (hdWGCNA) analysis to identify key immune cell subpopulations and module genes. Subsequently, ConsensusClusterPlus analysis was performed on the key module genes to identify different diseased subgroups in the osteoporosis (OP) training set samples. The immune characteristics between subgroups were evaluated using Cibersort, EPIC, and MCP counter algorithms. OP's hub genes were screened using 10 machine learning algorithms and 113 algorithm combinations. The relationship between hub genes and immunity and pathways was established by evaluating the immune and pathway scores of the training set samples through the ESTIMATE, MCP-counter, and ssGSEA algorithms. Real-time fluorescence quantitative PCR (RT-qPCR) testing was conducted on serum samples collected from osteoporosis patients and healthy adults.

RESULTS

In OP samples, the proportions of bone marrow-derived mesenchymal stem cells (BM-MSCs) and neutrophils increased significantly by 6.73% (from 24.01% to 30.74%) and 6.36% (from 26.82% to 33.18%), respectively. We found 16 intersection genes and four hub genes (DND1, HIRA, SH3GLB2, and F7). RT-qPCR results showed reduced expression levels of DND1, HIRA, and SH3GLB2 in clinical blood samples of OP patients. Moreover, the four hub genes showed positive correlations with neutrophils (0.65-0.90), immature B cells (0.76-0.92), and endothelial cells (0.79-0.87), while showing negative correlations with myeloid-derived suppressor cells (negative 0.54-0.73), T follicular helper cells (negative 0.71-0.86), and natural killer T cells (negative 0.75-0.85).

CONCLUSION

Neutrophils play a crucial role in the occurrence and development of osteoporosis. The four hub genes potentially inhibit metabolic activities and trigger inflammation by interacting with other immune cells, thereby significantly contributing to the onset and diagnosis of OP.

The role of mitophagy in metabolic diseases and its exercise intervention

Mitochondria are energy factories that sustain life activities in the body, and their dysfunction can cause various metabolic diseases that threaten human health. Mitophagy, an essential intracellular mitochondrial quality control mechanism, can maintain cellular and metabolic homeostasis by removing damaged mitochondria and participating in developing metabolic diseases. Research has confirmed that exercise can regulate mitophagy levels, thereby exerting protective metabolic effects in metabolic diseases. This article reviews the role of mitophagy in metabolic diseases, the effects of exercise on mitophagy, and the potential mechanisms of exercise-regulated mitophagy intervention in metabolic diseases, providing new insights for future basic and clinical research on exercise interventions to prevent and treat metabolic diseases.

Mechanisms of action and synergetic formulas of plant-based natural compounds from traditional Chinese medicine for managing osteoporosis: a literature review

Osteoporosis (OP) is a systemic skeletal disease prevalent in older adults, characterized by substantial bone loss and deterioration of microstructure, resulting in heightened bone fragility and risk of fracture. Traditional Chinese Medicine (TCM) herbs have been widely employed in OP treatment owing to their advantages, such as good tolerance, low toxicity, high efficiency, and minimal adverse reactions. Increasing evidence also reveals that many plant-based compounds (or secondary metabolites) from these TCM formulas, such as resveratrol, naringin, and ginsenoside, have demonstrated beneficial effects in reducing the risk of OP. Nonetheless, the comprehensive roles of these natural products in OP have not been thoroughly clarified, impeding the development of synergistic formulas for optimal OP treatment. In this review, we sum up the pathological mechanisms of OP based on evidence from basic and clinical research; emphasis is placed on the in vitro and preclinical in vivo evidence-based anti-OP mechanisms of TCM formulas and their chemically active plant constituents, especially their effects on imbalanced bone homeostasis regulated by osteoblasts (responsible for bone formation), osteoclasts (responsible for bone resorption), bone marrow mesenchymal stem cells as well as bone microstructure, angiogenesis, and immune system. Furthermore, we prospectively discuss the combinatory ingredients from natural products from these TCM formulas. Our goal is to improve comprehension of the pharmacological mechanisms of TCM formulas and their chemically active constituents, which could inform the development of new strategies for managing OP.

Anti-inflammatory and antioxidant traditional Chinese Medicine in treatment and prevention of osteoporosis

A metabolic bone disorder called osteoporosis is characterized by decreased bone mass and compromised microarchitecture. This condition can deteriorate bones and raise the risk of fractures. The two main causes of osteoporosis are an increase in osteoclast activity or quantity and a decrease in osteoblast viability. Numerous mechanisms, including estrogen shortage, aging, chemical agents, and decreased mechanical loads, have been linked to osteoporosis. Inflammation and oxidative stress have recently been linked to osteoporosis, according to an increasing number of studies. The two primary medications used to treat osteoporosis at the moment are bisphosphonates and selective estrogen receptor modulators (SERMs). These medications work well for osteoporosis brought on by aging and estrogen deprivation, however, they do not target inflammation and oxidative stress-induced osteoporosis. In addition, these drugs have some limitations that are attributed to various side effects that have not been overcome. Traditional Chinese medicine (TCM) has been applied in osteoporosis for many years and has a high safety profile. Therefore, in this review, literature related to botanical drugs that have an effect on inflammation and oxidative stress-induced osteoporosis was searched for. Moreover, the pharmacologically active ingredients of these herbs and the pathways were discussed and may contribute to the discovery of more safe and effective drugs for the treatment of osteoporosis.

Prognostic analysis and validation of diagnostic marker genes in patients with osteoporosis

Backgrounds

As a systemic skeletal dysfunction, osteoporosis (OP) is characterized by low bone mass and bone microarchitectural damage. The global incidences of OP are high.

Methods

Data were retrieved from databases like Gene Expression Omnibus (GEO), GeneCards, Search Tool for the Retrieval of Interacting Genes/Proteins (STRING), Gene Expression Profiling Interactive Analysis (GEPIA2), and other databases. R software (version 4.1.1) was used to identify differentially expressed genes (DEGs) and perform functional analysis. The Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression and random forest algorithm were combined and used for screening diagnostic markers for OP. The diagnostic value was assessed by the receiver operating characteristic (ROC) curve. Molecular signature subtypes were identified using a consensus clustering approach, and prognostic analysis was performed. The level of immune cell infiltration was assessed by the Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) algorithm. The hub gene was identified using the CytoHubba algorithm. Real-time fluorescence quantitative PCR (RT-qPCR) was performed on the plasma of osteoporosis patients and control samples. The interaction network was constructed between the hub genes and miRNAs, transcription factors, RNA binding proteins, and drugs.

Results

A total of 40 DEGs, eight OP-related differential genes, six OP diagnostic marker genes, four OP key diagnostic marker genes, and ten hub genes (TNF, RARRES2, FLNA, STXBP2, EGR2, MAP4K2, NFKBIA, JUNB, SPI1, CTSD) were identified. RT-qPCR results revealed a total of eight genes had significant differential expression between osteoporosis patients and control samples. Enrichment analysis showed these genes were mainly related to MAPK signaling pathways, TNF signaling pathway, apoptosis, and Salmonella infection. RT-qPCR also revealed that the MAPK signaling pathway (p38, TRAF6) and NF-kappa B signaling pathway (c-FLIP, MIP1β) were significantly different between osteoporosis patients and control samples. The analysis of immune cell infiltration revealed that monocytes, activated CD4 memory T cells, and memory and naïve B cells may be related to the occurrence and development of OP.

Conclusions

We identified six novel OP diagnostic marker genes and ten OP-hub genes. These genes can be used to improve the prognostic of OP and to identify potential relationships between the immune microenvironment and OP. Our research will provide insights into the potential therapeutic targets and pathogenesis of osteoporosis.