Comprehensive analysis of arachidonic acid metabolism-related genes in diagnosis and synovial immune in osteoarthritis: based on bulk and single-cell RNA sequencing data

Proteomic and lipidomic landscape of the infrapatellar fat pad and its clinical significance in knee osteoarthritis

Osteoarthritis (OA) is a prevalent joint disease that can be exacerbated by lipid metabolism disorders. The intra-articular fat pad (IFP) has emerged as an active participant in the pathological changes of knee OA (KOA). However, the proteomic and lipidomic differences between IFP tissues from KOA and control individuals remain unclear. Samples of IFP were collected from individuals with and without OA (n = 6, n = 6). Subsequently, these samples underwent liquid chromatography/mass spectrometry-based label-free quantitative proteomic and lipidomic analysis to identify differentially expressed proteins (DEPs) and lipid metabolites (DELMs). The DEPs were further subjected to enrichment analysis, and hub DEPs were identified using multiple algorithms. Additionally, an OA diagnostic model was constructed based on the identified hub DEPs or DELMs. Furthermore, CIBERSORT was utilized to investigate the correlation between hub protein expression and immune-related modules in IFP of OA. Our results revealed the presence of 315 DEPs and eight DELMs in IFP of OA patients compared to the control group. Enrichment analysis of DEPs highlighted potential alterations in pathways related to coagulation, complement, fatty acid metabolism, and adipogenesis. The diagnostic model incorporating four hub DEPs (AUC = 0.861) or eight DELMs (AUC = 0.917) exhibited excellent clinical validity for diagnosing OA. Furthermore, the hub DEPs were found to be associated with immune dysfunction in IFP of OA. This study presents a distinct proteomic and lipidomic landscape of IFP between individuals with OA and those without. These findings provide valuable insights into the molecular changes associated with potential mechanisms underlying OA.

An exploratory metabolomic study reveals the Dipsacus asper-Achyranthes bidentate herb pair against osteoarthritis by modulating imbalance in polyunsaturated fatty acids and energy metabolism

Osteoarthritis (OA) is a degenerative joint disease primarily affecting the cartilage. The therapeutic potential of the Dipsacus asper-Achyranthes bidentate herb pair for OA has been acknowledged, yet its precise mechanism remains elusive. In this study, we conducted a comprehensive analysis of metabolomic changes and therapeutic outcomes in osteoarthritic rats, employing a gas chromatography-mass spectrometry-based metabolomics approach in conjunction with histopathological and biochemical assessments. The rats were divided into six groups: control, model, positive control, Dipsacus asper treated, Achyranthes bidentata treated, and herb pair treated groups. Compared to the model group, significant reductions in levels of tumor necrosis factor-α (TNF-α), interleukin-1β (IL-1β), interleukin-6 (IL-6), and iNOS were observed in the treated groups. Multivariate statistical analyses were employed to investigate metabolite profile changes in serum samples and identify potential biomarkers, revealing 45 differential biomarkers, with eighteen validated using standard substances. These analytes exhibited excellent linearity across a wide concentration range (R2>0.9990), with intra- and inter-day precision RSD values below 4.69% and 4.83%, respectively. Recoveries of the eighteen analytes ranged from 93.97% to 106.59%, with RSD values under 5.72%, underscoring the method's reliability. Treatment with the herbal pair effectively restored levels of unsaturated fatty acids such as linoleic acid and arachidonic acid, along with glucogenic amino acids. Additionally, levels of phosphoric acid and citric acid were reversed, indicating restoration of energy metabolism. Collectively, these findings highlight the utility of metabolomic analysis in evaluating therapeutic efficacy and elucidating the underlying molecular mechanisms of herb pairs in OA treatment.

Identification of biomarkers and immune infiltration characterization of lipid metabolism-associated genes in osteoarthritis based on machine learning algorithms

Osteoarthritis (OA) is a prevalent degenerative condition commonly observed in the elderly, leading to consequential disability. Despite notable advancements made in clinical strategies for OA, its pathogenesis remains uncertain. The intricate association between OA and metabolic processes has yet to receive comprehensive exploration. In our investigation, we leveraged public databases and applied machine learning algorithms, including WGCNA, LASSO, RF, immune infiltration analysis, and pathway enrichment analysis, to scrutinize the role of lipid metabolism-associated genes (LAGs) in the OA. Our findings identified three distinct biomarkers, and evaluated their expression to assess their diagnostic value in the OA patients. The exploration of immune infiltration in these patients revealed an intricate relationship between immune cells and the identified biomarkers. In addition, in vitro experiments, including qRT-PCR, Western blot, chondrocyte lipid droplets detection and mitochondrial fatty acid oxidation measurement, further verified abnormal expressions of selected LAGs in OA cartilage and confirmed the correlation between lipid metabolism and OA.

Revealing PPP1R12B and COL1A1 as piRNA pathway genes contributing to abdominal aortic aneurysm through integrated analysis and experimental validation

BACKGROUND

Abdominal aortic aneurysm (AAA) is a permanent dilation of the abdominal aorta, with a high mortality rate when rupturing. Although lots of piRNA pathway genes (piRPGs) have recently been linked to both neoplastic and non-neoplastic illnesses, their role in AAA is still unknown. Utilizing integrative bioinformatics methods, this research discovered piRPGs as biomarkers for AAA and explore possible molecular mechanisms.

METHODS

The datasets were obtained from the Gene Expression Omnibus and piRPGs were identified from the Genecards database. The "limma" and "clusterProfiler" R-packages were used to discover differentially expressed genes and perform enrichment analysis, respectively. Hub piRPGs were further filtered using least absolute shrinkage and selection operator regression, random forests, as well as receiver operating characteristic curve. Additionally, multi-factor logistic regression (MLR), extreme gradient boosting (XGboost), and artificial neural network (ANN) were employed to construct prediction models. The relationship between hub piRPGs and immune infiltrating cells and sgGSEA were further studied. The expression of hub piRPGs was verified by qRT-PCR, immunohistochemistry, and western blotting in AAA and normal vascular tissues and analyzed by scRNA-seq in mouse AAA model. SRAMP and cMAP database were utilized for the prediction of N6-methyladenosine (m6A) targets therapeutic drug.

RESULTS

34 differentially expressed piRPGs were identified in AAA and enriched in pathways of immune regulation and gene silence. Three piRPGs (PPP1R12B, LRP10, and COL1A1) were further screened as diagnostic genes and used to construct prediction model. Compared with MLR and ANN, Xgboost showed better predictive ability, and PPP1R12B might have the ability to distinguish small and large AAA. Furthermore, the expression levels of PPP1R12B and COL1A1 were consistent with the results of bioinformatics analysis, and PPP1R12B showed a downward trend that may be related to m6A.

CONCLUSION

The results suggest that piRPGs might serve a significant role in AAA. PPP1R12B, COL1A1, and LRP10 had potential as diagnostic-specific biomarkers for AAA and performed better in XGboost model. The expression and localization of PPP1R12B and COL1A1 were experimentally verified. Besides, downregulation of PPP1R12B caused by m6A might contribute to the formation of AAA.

Osteoarthriris year in review 2023: genetics, genomics, and epigenetics

OBJECTIVE

To elucidate the scientific advances made in the last 12 months within the realm of osteoarthritis genetics, genomics, and epigenetics. This review paper highlights major research publications that enhance our current understanding of the role of genetics, genomics, and epigenetics in osteoarthritis.

METHODS

A systematic literature search was conducted on pubmed.ncbi.nlm.nih.gov on "March 17, 2023", using the following keywords: "osteoarthritis" in combination with any of these terms: "genetic(s)", "mutation(s)", "genomic(s)", "epigenetic(s)", "DNA methylation", "noncoding RNA", "lncRNA", "circular RNA", "microRNA", "transcriptomic(s)", "RNA sequencing", "single cell RNA sequencing", or "single nucleus RNA sequencing". The selection comprised original research articles published in the English language between the OARSI congresses of 2022 and 2023.

RESULTS

A total of 2178 research articles were identified, which subsequently reduced to 67 unique articles relevant to the field. Current trends in osteoarthritis genetics research involve meta-analyses of various cohorts to explore the impact of gene variants on osteoarthritis-related outcomes, such as pain. Early developmental changes within the joint were also found to influence osteoarthritis through genetic variations. Researchers also prioritize testing the mechanisms and functions of miRNAs, circRNAs, and lncRNAs. Potential drug targets began to emerge; however, independent validation studies are lacking. Single cell RNA sequencing studies revealed unique immune cell populations in the knee; however, no study reported single nucleus RNA sequencing analysis.

CONCLUSIONS

This review focused on recent advances in the above-mentioned themes within the field of osteoarthritis. These advances improve our understanding of the disease's complexity and guide us toward functional assessments of genetic/epigenetic outcomes and toward their translational and clinical applications.

Berberine regulates intestinal microbiome and metabolism homeostasis to treat ulcerative colitis

AIMS

This study aims to investigate the effects of berberine (BBR) on the intestinal microbiome (IM) and serum metabolome in ulcerative colitis (UC). Furthermore, the underlying molecular mechanisms of BBR in treating UC also will be explored systematically.

MATERIALS AND METHODS

A multi-omics approach that integrates the 16s rDNA, serum metabolome, transcriptomics and bioinformatics was profiled to investigate the potential effects of BBR on the IM, serum metabolites and metabolic pathways, and gene expression. In addition, BBR-induced fecal microbiota transplantation (BBR_FMT) was conducted in pseudo germ-free mice combined with the UC model to explore the effects of the IM on metabolic pathways and gene expression. The results of the transcriptomics and metabolic pathway-related genes were further examined by real-time PCR and western blot.

KEY FINDINGS

BBR ameliorated the community of IM and significantly promoted the abundance of f__Muribaculaceae, Bacteroides, Dubosiella, Allobaculum and Akkermansia. The metabolic profiles in UC mice were significantly modulated by BBR treatment. Furthermore, the inflammation-related metabolites and metabolic pathways in serum were negatively correlated with the abundance of Bacteroides and Akkermansia, which were induced by BBR treatment. BBR_FMT significantly inhibited the arachidonic acid (AA) metabolism pathway and its multiple markers with the mediation of the IM.

SIGNIFICANCE

BBR ameliorated serum metabolic homeostasis by regulating the IM. The inhibition of the AA metabolism pathway and its multiple markers was one of the mechanisms of BBR in the treatment of UC.

Exploring biomarkers associated with severity of knee osteoarthritis in Southern China using widely targeted metabolomics

BACKGROUND

Metabolomics is a tool to study the pathogenesis of diseases and their associated metabolites, but there are still insufficient metabolomic studies on severe knee osteoarthritis.To investigate the differences in serum metabolites between healthy populations and knee osteoarthritis (KOA) patients in Southern China using widely targeted metabolomics, and to explore biomarkers and their metabolic pathways that could be associated with the severity of KOA.

METHODS

There were 10 healthy individuals in the control group and 32 patients with KOA. According to the Kellgren-Lawrence (KL) grading system, KOA was further divided into mild (n = 13, KL grade 1 and 2) and severe (n = 19, KL grade 3 and 4). Serum samples from all participants were collected and analyzed metabolomics based on ultra-performance liquid chromatography/electrospray ionization/tandem mass spectrometry. We screened for differential metabolites between patients and controls, and between mild and severe KOA. We explored the metabolic pathways involved in differential metabolism using the Kyoto Encyclopedia of Genes and Genomes database.

RESULTS

Sixty-one metabolites were differentially expressed in the sera of the patient group compared with the control group (45 upregulated and 16 downregulated). Analysis of the mild and severe KOA groups showed a total of 12 differential metabolites. Receiver operating characteristic curve analysis showed N-alpha-acetyl-L-asparagine was a good predictor of advanced osteoarthritis(OA).Differential metabolites are enriched in multiple pathways such as arachidonic acid metabolism.

CONCLUSION

Widely targeted metabolomics found that upregulation of the amino acid metabolite N-α-acetyl-L-asparagine was significantly associated with severe KOA and could be a biomarker for predicting severity of KOA. Arachidonic acid metabolism may play an important role in patients with severe KOA.

Single-Cell RNA Sequencing and Microarray Analysis Reveal the Role of Lipid-Metabolism-Related Genes and Cellular Immune Infiltration in Pre-Eclampsia and Identify Novel Biomarkers for Pre-Eclampsia

Pre-eclampsia (PE) is a gestational hypertensive disorder that is characterized by hypertension and proteinuria, typically occurring after 20 weeks of gestation. Despite its global impact on pregnant women, the precise pathogenic mechanisms of PE remain unclear. Dysregulated lipid metabolism and immune cell infiltration contribute to PE development. Our study aimed to identify lipid-metabolism-related genes (LMRG-PEs) and investigate their association with immune infiltration. We utilized the "Seurat" R package for data quality control, cell clustering, and marker gene identification. The "SingleR" package enabled the matching of marker genes to specific cell types. Pseudotemporal ordering analysis was conducted using the "Monocle" package. Weighted correlation network analysis (WGCNA), gene set variation analysis (GSVA), and gene set enrichment analysis (GSEA) approaches were employed to explore lipid-metabolism-related genes, while potential targeted drugs were predicted using the drug-gene interaction database (DGIdb). Hub gene expression was validated through RT-qPCR. By analyzing single-cell RNA sequencing data, we identified and classified 20 cell clusters into 5 distinct types. Differential gene expression analysis revealed 186 DEGs. WGCNA identified 9 critical modules and 265 genes significantly associated with PE diagnosis, emphasizing the importance of the core genes PLA2G7 and PTGS2. RT-qPCR confirmed the significantly decreased expression of PLA2G7 and PTGS2 in PE patient tissues. These findings offer valuable insights into the molecular mechanisms of PE, particularly those involving lipid metabolism and immune infiltration. The identified hub genes have potential as therapeutic targets and biomarkers for future research and clinical applications.

Therapeutic potential of Coptis chinensis for arthritis with underlying mechanisms

Arthritis is a common degenerative disease of joints, which has become a public health problem affecting human health, but its pathogenesis is complex and cannot be eradicated. Coptis chinensis (CC) has a variety of active ingredients, is a natural antibacterial and anti-inflammatory drug. In which, berberine is its main effective ingredient, and has good therapeutic effects on rheumatoid arthritis (RA), osteoarthritis (OA), gouty arthritis (GA). RA, OA and GA are the three most common types of arthritis, but the relevant pathogenesis is not clear. Therefore, molecular mechanism and prevention and treatment of arthritis are the key issues to be paid attention to in clinical practice. In general, berberine, palmatine, coptisine, jatrorrhizine, magnoflorine and jatrorrhizine hydrochloride in CC play the role in treating arthritis by regulating Wnt1/β-catenin and PI3K/AKT/mTOR signaling pathways. In this review, active ingredients, targets and mechanism of CC in the treatment of arthritis were expounded, and we have further explained the potential role of AHR, CAV1, CRP, CXCL2, IRF1, SPP1, and IL-17 signaling pathway in the treatment of arthritis, and to provide a new idea for the clinical treatment of arthritis by CC.