Key genes associated with osteoporosis revealed by genome wide gene expression analysis

Serum and urine metabolomics study revealed the amelioration of Gynura bicolor extract on high fat diet-fed and streptozotocin-induced type 2 diabetic mice based on UHPLC-MS/MS

Type 2 diabetes mellitus (T2DM) has been the most prevalent disease and has become a serious public health threat worldwide. Gynura bicolor (Willd.) DC. (GB) contains a variety of nutrients and possesses numerous activities, which might benefit those with diabetes. The current study aimed to confirm the improvement of metabolic disorders and explore the potential mechanism of GB in high fat diet-fed (HFD) and streptozotocin (STZ)-induced T2DM mice. The aboveground sample of GB was extracted with alcohol, and identified by highperformance liquid chromatography (HPLC) and liquid chromatography-Mass Spectrometry/Mass Spectrometry (LC-MS/MS) analysis. HFD and STZ-induced T2DM mice were administrated with GB extract. Biochemical and histopathologic examinations were conducted, and metabolomics evaluation was performed in serum and urine. GB significantly reduced body weight and liver weight, reversed hyperlipidemia, hyperglycemia, insulin resistance, oxidative stress and inflammation, improved hepatic histopathological changes and lipid deposition and mitigated liver injury in T2DM mice. Serum and urine metabolomics demonstrated a variety of significantly disturbed metabolites in T2DM and these changes were reversed after GB administration, including 13S-hydroxyoctadecadienoic acid, arachidonic acid, L-Valine and so on. According to the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, the overlapping enriched pathways in the normal control group and GB group were identified, including linoleic acid metabolism, PPAR signaling pathway, protein digestion and absorption, biosynthesis of amino acids and so on. This study demonstrates that the ethanol extract of GB remarkably attenuates metabolic disorders and maintains the dynamic balance of metabolites in T2DM, providing a scientific basis for GB in the treatment of T2DM and metabolism diseases.

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.

Screening pathways and hub genes involved in osteoclastogenesis by gene expression analysis of circulating monocytes based on Gibbs sampling

Differential expression pathways and hub genes in circulating monocytes from healthy Chinese women with high peak bone mass (PBM) vs. low PBM were explored using a Markov chain Monte Carlo (MCMC) algorithm. Human circulating monocytes transcription profiling (containing 14 samples with high PBM and 12 samples with low PBM) and KEGG pathways were all downloaded from the public database. Initial state of all the pathways were constructed and Gibbs sampling was performed to obtain a Markov chain and the posterior values of all the pathways were calculated. The probability (α) of occurrence of each pathway was calculated based on the posterior value and it was adjusted by taking gene expression variation into account. Pathways with α_adj >0.8 were considered as differentially expressed pathways. Then, these steps were performed again on all the genes in the differentially expressed pathways to find the hub genes in the differential pathways. After Gibbs sampling, neuroactive ligand-receptor interaction (hsa04080) with α_adj = 0.986 was screened out as the differentially expressed pathway. Analyzing the genes in this pathway, three genes (neurotensin, tachykinin receptor 3 and follicle-stimulating hormone receptor) with α_adj >0.8 were identified as hub genes in circulating monocytes which may associate with osteoporosis development. One pathway and three genes which may possess close relationship with osteoporosis development were found in this study. These results provide insights into our understanding of the role of circulating monocytes in osteoporosis development.