Discovery of Potential Biomarkers for Postmenopausal Osteoporosis Based on Untargeted GC/LC-MS

Discovery of potential biomarkers for osteoporosis using LC/GC-MS metabolomic methods

Purpose

For early diagnosis of osteoporosis (OP), plasma metabolomics of OP was studied by untargeted LC/GC-MS in a Chinese elderly population to find possible diagnostic biomarkers.

Methods

A total of 379 Chinese community-dwelling older adults aged ≥65 years were recruited for this study. The BMD of the calcaneus was measured using quantitative ultrasound (QUS), and a T value ≤-2.5 was defined as OP. Twenty-nine men and 47 women with OP were screened, and 29 men and 36 women were matched according to age and BMI as normal controls using propensity matching. Plasma from these participants was first analyzed by untargeted LC/GC-MS, followed by FC and P values to screen for differential metabolites and heatmaps and box plots to differentiate metabolites between groups. Finally, metabolic pathway enrichment analysis of differential metabolites was performed based on KEGG, and pathways with P ≤ 0.05 were selected as enrichment pathways.

Results

We screened metabolites with FC>1.2 or FC<1/1.2 and P<0.05 and found 33 differential metabolites in elderly men and 30 differential metabolites in elderly women that could be potential biomarkers for OP. 2-Aminomuconic acid semialdehyde (AUC=0.72, 95% CI 0.582-0.857, P=0.004) is highly likely to be a biomarker for screening OP in older men. Tetradecanedioic acid (AUC=0.70, 95% CI 0.575-0.818, P=0.004) is highly likely to be a biomarker for screening OP in older women.

Conclusion

These findings can be applied to clinical work through further validation studies. This study also shows that metabolomic analysis has great potential for application in the early diagnosis and recurrence monitoring of OP in elderly individuals.

Identifying potential ferroptosis key genes for diagnosis and treatment of postmenopausal osteoporosis through competitive endogenous RNA network analysis

Objective

Postmenopausal osteoporosis (PMOP) is a common systemic metabolic bone disorder that is owing to the reduced estrogen secretion and imbalance of bone absorption and bone formation in postmenopausal women. Ferroptosis has been identified as a novel modulatory mechanism of osteoporosis. Nevertheless, the particular modulatory mechanism between ferroptosis and PMOP is still unclear. The objective of the current investigation was to detect potential biomarkers connected to ferroptosis in PMOP and discover its probable mechanism through bioinformatics.

Methods

We downloaded PMOP-related microarray datasets from the database of Gene Expression Omnibus (GEO) and obtained the differentially expressed genes (DEGs). Utilizing bioinformatics analysis, the DEGs were intersected with the ferroptosis dataset to obtain ferroptosis-connected mRNAs. Enrichment analysis employing KOBAS 3.0 was conducted to comprehend the biological functions and enrichment pathways of the DEGs. The generation of the protein-protein interaction (PPI) network was conducted with the aim of identifying central genes. Lastly, the coexpression and competitive endogenous RNA (ceRNA) networks were built using Cytoscape. With the help of external datasets GSE56815 to verify the reliability of the hub genes by plotting ROC curves.

Results

We identified 178 DE microRNAs (miRNAs), 138 DE circular RNAs (circRNAs), and 86 ferroptosis-related mRNAs. Enrichment analysis exhibited that mRNAs were primarily connected with the signaling pathways of PI3K/Akt, metabolism, mTOR, FoxO, HIF-1, AMPK, MAPK, ferroptosis, VEGF, and NOD-like receptors. Generation of the PPI network detected eight hub genes. The circRNA/miR-23b-3p/PTEN axis may relieve PMOP by inhibiting ferroptosis through targeting the pathway of PI3K/Akt signaling, which is a vital modulatory pathway for PMOP progression. Moreover, the ROC curves ultimately indicates that the four hub genes have greater diagnostic importance in PMOP samples in contrast to the normal group samples, which may be possible markers for PMOP diagnosis.

Conclusions

Bioinformatics analysis identified four hub genes, namely, PTEN, SIRT1, VEGFA, and KRAS, as potential biomarkers for PMOP diagnosis and management. Moreover, the circRNA/miR-23b-3p/PTEN axis may relieve PMOP by suppressing ferroptosis through targeting the pathway of PI3K/Akt signaling, providing a new avenue to explore the pathogenesis of PMOP.

Chemical characterization and discovery of novel quality markers in Citrus aurantium L. fruit from traditional cultivation areas in China using GC-MS-based cuticular waxes analysis

Citrus aurantium L. fruit is a commonly used Chinese medicine whose therapeutic effects tends to be affected by growing conditions. In order to gain insights into the effects of growing location on the cuticular wax composition of C. aurantium L. fruit, we analyzed the differences in the wax composition of its fruits collected from different regions. The findings showed that the cuticular waxes in the fruit peels were mainly composed of fatty acids, which differed quantitatively in the chemical profiles of C. aurantium L. samples from different geographical conditions. Particularly, the concentrations of linoleic acid and stearic acid in the total component content of the fruit peel were above 1%, with a greater level in the geo-authentic samples. Thus, GC-MS-based wax analysis was first used for the chemical characterization and quantification of cuticular waxes, which could be considered as a rapid way for evaluating the quality of medicinal fruits.

Distinct Metabolites in Osteopenia and Osteoporosis: A Systematic Review and Meta-Analysis

Multiple studies have indicated that distinct metabolites are involved in the occurrence and development of osteopenia (ON) and osteoporosis (OP); however, these metabolites in OP and ON have not yet been classified and standardized. This systematic review and meta-analysis included 21 articles aiming to investigate the distinct metabolites in patients with ON and OP. The quality of the included articles was generally high; seventeen studies had >7 stars, and the remaining four received 6 stars. This systematic review showed that three metabolites (phosphatidylcholine (PC) (lipid metabolites), galactose (carbohydrate metabolites), and succinic acid (other metabolites)) increased, four (glycylglycine (gly-gly), cystine (amino acids), sphingomyelin (SM) (lipid metabolites) and glucose (carbohydrate metabolites)) decreased, and five (glutamine, hydroxyproline, taurine (amino acids), lysophosphatidylcholine (LPC) (lipid metabolites), and lactate (other metabolites)) had conflicting directions in OP/ON. The results of the meta-analysis show that gly-gly (MD = -0.77, 95%CI -1.43 to -0.11, p = 0.02) and cystine (MD = -5.52, 95%CI -7.35 to -3.68, p < 0.00001) decreased in the OP group compared with the healthy control group. Moreover, LPC (MD = 1.48, 95%CI 0.11 to 2.86, p = 0.03) increased in the OP group compared with the healthy control group. These results indicate that distinct metabolites were associated with ON and OP, which could be considered a predictor for OP.

Application and prospect of metabolomics in the early diagnosis of osteoporosis: a narrative review

This paper reviews the application of metabolomics in the early diagnosis of osteoporosis in recent years. The authors searched electronic databases for the keywords "metabolomics", "osteoporosis" and "biomarkers", then analyzed the relationship between functional markers and osteoporosis using categorical summarization. Lipid metabolism, amino acid metabolism and energy metabolism are closely related to osteoporosis development and can become early diagnostic markers of the condition. However, the existing studies in metabolomics suffer from varying application methods, difficulty in identifying isomers, small study cohorts and insufficient research on metabolic mechanisms. Consequently, it is important for future research to focus on broadening and standardizing the scope of the application of metabolomics. High-quality studies on a large scale should also be conducted while promoting the early diagnosis of osteoporosis in a more precise, comprehensive and sensitive manner.

Identification of urine biomarkers associated with early puberty in children: An untargeted metabolomics analysis

A trend toward earlier pubertal maturation in both sexes has been shown in many countries. Early puberty affects an increasing proportion of children for reasons that remain obscure. Novel candidate biomarkers are strongly needed. We sought to apply untargeted metabolomic profiling to identify triggering mechanisms and candidate biomarkers in children with early puberty. Participants aged 7 - 12 years old were recruited directly from two elementary schools of Bengbu, Anhui Province, China, from Feb 2021 to May 2021. Early puberty was determined by breast and testicular development at baseline (May 2021) and 6-month later. Ultra-high-performance liquid chromatography-based untargeted metabolomic profiling was performed on urine samples of children with early puberty and control subjects. Metabolomic profiling for early puberty in a sex dependent manner. For boys, we identified several perturbed pathways, including histidine metabolism, glycine, serine and threonine metabolism, and selenoamino acid metabolism, associated with early puberty. In contrast, there were differences in pyruvate metabolism, one carbon pool by folate, and D-glutamine and D-glutamate metabolism pathways in girls with early puberty compared with controls. In addition, 4-hydroxyhippuric acid and 5-methoxytryptophol were shown as potential independent diagnostic biomarker for early puberty in boys, 3-hydroxybenzoic acid and glutaminylproline were shown as early biomarker for early puberty in girls, achieving area under the ROC curve of 0.71 and 0.72 in discriminating early puberty boys, and 0.70 and 0.74 in discriminating early puberty girls from controls. Through metabolomic analysis, we have identified metabolic perturbations and potential biomarkers of early puberty.

Plasma Metabolomic Profiling after Feeding Dried Distiller's Grains with Solubles in Different Cattle Breeds

Dried distiller's grains with solubles (DDGS) are rich in nutrients and can enhance animals' growth and immunity. However, there are few reports on the effects of a diet of DDGS on plasma metabolism and the related action pathways in domestic animals. In this study, groups of Guanling yellow cattle (GY) and Guanling crossbred cattle (GC) having a basal diet served as the control groups (GY-CG and GC-CG), and DDGS replacing 25% of the diet of GY and GC served as the replacement groups (GY-RG and GC-RG), with three cattle in each group. Plasma samples were prepared for metabolomic analysis. Based on multivariate statistical and univariate analyses, differential metabolites and metabolic pathways were explored. Twenty-nine significantly different metabolites (p < 0.05) were screened in GY-RG compared with those in GY-CG and were found to be enriched in the metabolic pathways, including choline metabolism in cancer, linolenic acid metabolism, and amino acid metabolism. Nine metabolites showed significant differences (p < 0.05) between GC-RG and GC-CG and were mainly distributed in the metabolic pathways of choline metabolism in cancer, glycerophospholipid metabolism, prostate cancer metabolism, and gonadotropin-releasing hormone (GnRH) secretion. These results suggest that a DDGS diet may promote healthy growth and development of experimental cattle by modulating these metabolic pathways. Our findings not only shed light on the nutritional effects of the DDGS diet and its underlying mechanisms related to metabolism but also provide scientific reference for the feed utilization of DDGS.

A metabonomic study to explore potential markers of asymptomatic hyperuricemia and acute gouty arthritis

BACKGROUND

Acute gouty arthritis (AGA) is a metabolic disease with acute arthritis as its main manifestation. However, the pathogenesis of asymptomatic hyperuricemia (HUA) to AGA is still unclear, and metabolic markers are needed to early predict and diagnose. In this study, gas chromatography (GC)/liquid chromatography (LC)-mass spectrometry (MS) was used to reveal the changes of serum metabolites from healthy people to HUA and then to AGA, and to find the pathophysiological mechanism and biological markers.

METHODS

Fifty samples were included in AGA, HUA, and healthy control group, respectively. The metabolites in serum samples were detected by GC/LC-MS. According to the statistics of pairwise grouping, the statistically significant differential metabolites were obtained by the combination of multidimensional analysis and one-dimensional analysis. Search the selected metabolites in KEGG database, determine the involved metabolic pathways, and draw the metabolic pathway map in combination with relevant literature.

RESULTS

Using metabonomics technology, 23 different serum metabolic markers related to AGA and HUA were found, mainly related to uric acid metabolism and inflammatory response caused by HUA/AGA. Three of them are completely different from the previous gout studies, nine metabolites with different trends from conventional inflammation.

CONCLUSIONS

In conclusion, we analyzed 150 serum samples from AGA, HUA, and healthy control group by GC/LC-MS to explore the changes of these differential metabolites and metabolic pathways, suggesting that the disease progression may involve the changes of biomarkers, which may provide a basis for disease risk prediction and early diagnosis.

The Hub Genes Related to Osteoporosis Were Identified by Bioinformatics Analysis

Osteoporosis (OP) is commonly encountered, which is a kind of systemic injury of bone mass and microstructure, leading to brittle fractures. With the aging of the population, this disease will pose a more serious impact on medical, social, and economic aspects, especially postmenopausal osteoporosis (PMOP). This study is aimed at figuring out potential therapeutic targets and new biomarkers in OP via bioinformatics tools. After differentially expressed gene (DEG) analysis, we successfully identified 97 upregulated and 172 downregulated DEGs. They are mainly concentrated in actin binding, regulation of cytokine production, muscle cell promotion, chemokine signaling pathway, and cytokine-cytokine receiver interaction. According to the diagram of protein-protein interaction (PPI), we obtained 10 hub genes: CCL5, CXCL10, EGFR, HMOX1, IL12B, CCL7, TBX21, XCL1, PGR, and ITGA1. Expression analysis showed that Egfr, Hmox1, and Pgr were significantly upregulated in estrogen-treated osteoporotic patients, while Ccl5, Cxcl10, Il12b, Ccl7, Tbx21, Xcl1, and Itga1 were significantly downregulated. In addition, the analysis results of Pearson's correlation revealed that CCL7 has a strong positive association with IL12b, TBX21, and CCL5 and so was CCL5 with IL12b. Conversely, EGFR has a strong negative association with XCL1 and CXCL10. In conclusion, this study screened 10 hub genes related to OP based on the GEO database, laying a biological foundation for further research on new biomarkers and potential therapeutic targets in OP.

A Multi-Omics Analysis Reveals Anti-Osteoporosis Mechanism of Four Components from Crude and Salt-Processed Achyranthes bidentata Blume in Ovariectomized Rats

The root of Achyranthes bidentata Blume (AB) is a well-known traditional Chinese medicine for treating osteoporosis. Plenty of studies focused on the pharmacological mechanism of the whole extract; however, the contribution of different components to the anti-osteoporosis effect remains unknown. The aim of this study is to explore the anti-osteoporosis mechanism of different components of crude and salt-processed AB under the guidance of network pharmacology, metabolomics, and microbiomics. First, network pharmacology analysis was applied to constructing the compound-target-disease network of AB to provide a holistic view. Second, the anti-osteoporosis effects of the four components were evaluated in female Wistar rats. The subjects were divided into a normal group, a model group, a 17α-estradiol (E2)-treated group, a polysaccharide-component-treated groups, and a polysaccharide-knockout-component-treated groups. All the serum, urine, and feces samples of the six groups were collected after 16 weeks of treatment. Biochemical and microcomputed tomography (μCT) parameters were also acquired. Coupled with orthogonal partial least-squares discrimination analysis, one dimensional nuclear magnetic resonance (NMR) was used to monitor serum metabolic alterations. A total of twenty-two biomarkers, including lipids, amino acids, polyunsaturated fatty acids, glucose, and so on were identified for the different components-treated groups. Through pathway analysis, it is indicated that glyoxylate and dicarboxylate metabolism, glycine, serine, and threonine metabolism, alanine, aspartate, and glutamate metabolism, d-glutamine, and d-glutamate metabolism were the major intervened pathways. Levels of these biomarkers shifted away from the model group and were restored to normal after treatment with the four components. In addition, 16S rDNA sequencing demonstrated that the abundance of Anaerofilum, Rothia, and Turicibacter bacteria was positively correlated with an anti-osteoporosis effect, whereas the abundance of Oscillospira was negatively correlated. The osteoprotective effect of the polysaccharide components of crude and salt-processed AB is related to the regulation of the abundance of these gut microbiota.