Role of the major histocompatibility complex class II protein presentation pathway in bone immunity imbalance in postmenopausal osteoporosis

Wang, Xiaoning; Zhang, Xin; Han, Yidan; Duan, Xinwei; Wang, Jianchang; Yan, Hui; Wang, Shanshan; Xu, Yunteng; Zhu, Zaishi; Wang, Lili; Huang, Yanfeng; Lin, Qing; Xue, Tao; Zhuo, Junkuan; Zhang, Haifeng; Mao, Min; Gou, Weiying; Yi, Zhouping; Li, Xihai

doi:10.3389/fendo.2022.876067

Cited by 5 publications

(5 citation statements)

References 79 publications

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“…Notably, OP is often linked with chronic inflammatory conditions [ 43 ]. The decline in estrogen during menopause and aging-related changes are known to exacerbate OP by enhancing osteoclastogenic inflammatory cytokine production [ 44 ]. Inflammatory rheumatic diseases, which involve both local and systemic bone loss, serve as prime examples of the deep-seated interplay between the immune system and bone health, often leading to osteoclast hyperactivation and a disrupted balance in bone dynamics [ 45 , 46 ].…”

Section: Discussionmentioning

confidence: 99%

Integration of bioinformatics and machine learning approaches for the validation of pyrimidine metabolism-related genes and their implications in immunotherapy for osteoporosis

Feng,

Wu,

Zhang

2024

BMC Musculoskelet Disord

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Background Osteoporosis (OP), the “silent epidemic” of our century, poses a significant challenge to public health, predominantly affecting postmenopausal women and the elderly. It evolves from mild symptoms to pronounced severity, stabilizing eventually. Unique among OP’s characteristics is the altered metabolic profile of affected cells, particularly in pyrimidine metabolism (PyM), a crucial pathway for nucleotide turnover and pyrimidine decomposition. While metabolic adaptation is acknowledged as a therapeutic target in various diseases, the specific role of PyM genes (PyMGs) in OP’s molecular response remains to be clarified. Methods In pursuit of elucidating and authenticating PyMGs relevant to OP, we embarked on a comprehensive bioinformatics exploration. This entailed the integration of Weighted Gene Co-expression Network Analysis (WGCNA) with a curated list of 37 candidate PyMGs, followed by the examination of their biological functions and pathways via Gene Set Variation Analysis (GSVA). The Least Absolute Shrinkage and Selection Operator (LASSO) technique was harnessed to identify crucial hub genes. We evaluated the diagnostic prowess of five PyMGs in OP detection and explored their correlation with OP’s clinical traits, further validating their expression profiles through independent datasets (GSE2208, GSE7158, GSE56815, and GSE35956). Results Our analytical rigor unveiled five PyMGs—IGKC, TMEM187, RPS11, IGLL3P, and GOLGA8N—with significant ties to OP. A deeper dive into their biological functions highlighted their roles in estrogen response modulation, cytosolic calcium ion concentration regulation, and GABAergic synaptic transmission. Remarkably, these PyMGs emerged as potent diagnostic biomarkers for OP, distinguishing affected individuals with substantial accuracy. Conclusions This investigation brings to light five PyMGs intricately associated with OP, heralding new avenues for biomarker discovery and providing insights into its pathophysiological underpinnings. These findings not only deepen our comprehension of OP’s complexity but also herald the advent of more refined diagnostic and therapeutic modalities.

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Section: Discussionmentioning

confidence: 99%

Integration of bioinformatics and machine learning approaches for the validation of pyrimidine metabolism-related genes and their implications in immunotherapy for osteoporosis

Feng,

Wu,

Zhang

2024

BMC Musculoskelet Disord

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“…The MHC-II molecule is a central molecule in the protein presentation pathway. It binds to processed short peptides and presents them to T lymphocytes, activating them to become effector T cells [ 54 ]. Abcouwer et al [ 55 ] Minocycline was particularly effective in decreasing the appearance of MHCII+ inflammatory leukocytes and reduced leukocyte adhesion and invasion, as well as vascular permeability in RIR.…”

Section: Discussionmentioning

confidence: 99%

Integrated bioinformatics analysis of retinal ischemia/reperfusion injury in rats with potential key genes

Qing,

Lo,

et al. 2024

BMC Genomics

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The tissue damage caused by transient ischemic injury is an essential component of the pathogenesis of retinal ischemia, which mainly hinges on the degree and duration of interruption of the blood supply and the subsequent damage caused by tissue reperfusion. Some research indicated that the retinal injury induced by ischemia-reperfusion (I/R) was related to reperfusion time.In this study, we screened the differentially expressed circRNAs, lncRNAs, and mRNAs between the control and model group and at different reperfusion time (24h, 72h, and 7d) with the aid of whole transcriptome sequencing technology, and the trend changes in time-varying mRNA, lncRNA, circRNA were obtained by chronological analysis. Then, candidate circRNAs, lncRNAs, and mRNAs were obtained as the intersection of differentially expression genes and trend change genes. Importance scores of the genes selected the key genes whose expression changed with the increase of reperfusion time. Also, the characteristic differentially expressed genes specific to the reperfusion time were analyzed, key genes specific to reperfusion time were selected to show the change in biological process with the increase of reperfusion time.As a result, 316 candidate mRNAs, 137 candidate lncRNAs, and 31 candidate circRNAs were obtained by the intersection of differentially expressed mRNAs, lncRNAs, and circRNAs with trend mRNAs, trend lncRNAs and trend circRNAs, 5 key genes (Cd74, RT1-Da, RT1-CE5, RT1-Bb, RT1-DOa) were selected by importance scores of the genes. The result of GSEA showed that key genes were found to play vital roles in antigen processing and presentation, regulation of the actin cytoskeleton, and the ribosome. A network included 4 key genes (Cd74, RT1-Da, RT1-Bb, RT1-DOa), 34 miRNAs and 48 lncRNAs, and 81 regulatory relationship axes, and a network included 4 key genes (Cd74, RT1-Da, RT1-Bb, RT1-DOa), 9 miRNAs and 3 circRNAs (circRNA_10572, circRNA_03219, circRNA_11359) and 12 regulatory relationship axes were constructed, the subcellular location, transcription factors, signaling network, targeted drugs and relationship to eye diseases of key genes were predicted. 1370 characteristic differentially expressed mRNAs (spec_24h mRNA), 558 characteristic differentially expressed mRNAs (spec_72h mRNA), and 92 characteristic differentially expressed mRNAs (spec_7d mRNA) were found, and their key genes and regulation networks were analyzed.In summary, we screened the differentially expressed circRNAs, lncRNAs, and mRNAs between the control and model groups and at different reperfusion time (24h, 72h, and 7d). 5 key genes, Cd74, RT1-Da, RT1-CE5, RT1-Bb, RT1-DOa, were selected. Key genes specific to reperfusion time were selected to show the change in biological process with the increased reperfusion time. These results provided theoretical support and a reference basis for the clinical treatment.

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“…The M2 phenotype is selectively activated macrophages.It is involved in osteogenic metabolism by stimulating bone marrow mesenchymal stem cells to differentiate into OBs. The M1 and M2 phenotypes are usually associated with bone catabolism and anabolism, respectively (10,11). One study showed that M1/M2 macrophage ratio increased in OVX osteoporosis mouse model (26), reflecting that estrogen deficiency mediated macrophage ratio changes may be closely related to osteoporosis.…”

Section: Discussionmentioning

confidence: 99%

“…Although OP is still considered an endocrine disease, a growing number of studies now show that it is closely related to immune and inflammatory states (9)(10)(11). Since bone and the immune system share a common developmental niche, the developmental remodelling of bone is affected by the immune system, and innate and adaptive immune cells can participate in the pathogenesis of OP by producing proinflammatory mediators (12).…”

Section: Introductionmentioning

confidence: 99%

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Identification of key immune genes of osteoporosis based on bioinformatics and machine learning

Hao,

Xinqi,

Weicheng

et al. 2023

Front. Endocrinol.

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IntroductionImmunity is involved in a variety of bone metabolic processes, especially osteoporosis. The aim of this study is to explore new bone immune-related markers by bioinformatics method and evaluate their ability to predict osteoporosis.MethodsThe mRNA expression profiles were obtained from GSE7158 in Gene expression Omnibus (GEO), and immune-related genes were obtained from ImmPort database (https://www.immport.org/shared/). immune genes related to bone mineral density(BMD) were screened out for differential analysis. protein-protein interaction (PPIs) networks were used to analyze the interrelationships between different immune-related genes (DIRGs). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of DIRGs function were performed. A least absolute shrinkage and selection operation (LASSO) regression model and multiple Support Vector Machine-Recursive Feature Elimination (mSVM-RFE) model were constructed to identify the candidate genes for osteoporosis prediction The receiver operator characteristic (ROC) curves were used to validate the performances of predictive models and candidate genes in GEO database (GSE7158,GSE13850).Through the RT - qPCR verify the key genes differentially expressed in peripheral blood mononuclear cells Finally, we constructed a nomogram model for predicting osteoporosis based on five immune-related genes. CIBERSORT algorithm was used to calculate the relative proportion of 22 immune cells.ResultsA total of 1158 DEGs and 66 DIRGs were identified between high-BMD and low-BMD women. These DIRGs were mainly enriched in cytokine−mediated signaling pathway, positive regulation of response to external stimulus and the cellular components of genes are mostly localized to external side of plasma membrane. And the KEGG enrichment analysis were mainly involved in Cytokine−cytokine receptor interaction, PI3K−Akt signaling pathway, Neuroactive ligand−receptor interaction,Natural killer cell mediated cytotoxicity. Then five key genes (CCR5, IAPP, IFNA4, IGHV3-73 and PTGER1) were identified and used as features to construct a predictive prognostic model for osteoporosis using the GSE7158 dataset.ConclusionImmunity plays an important role in the development of osteoporosis.CCR5, IAPP, IFNA4, IGHV3-73 and PTGER1were play an important role in the occurrences and diagnosis of OP.

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Role of the major histocompatibility complex class II protein presentation pathway in bone immunity imbalance in postmenopausal osteoporosis

Cited by 5 publications

References 79 publications

Integration of bioinformatics and machine learning approaches for the validation of pyrimidine metabolism-related genes and their implications in immunotherapy for osteoporosis

Integration of bioinformatics and machine learning approaches for the validation of pyrimidine metabolism-related genes and their implications in immunotherapy for osteoporosis

Integrated bioinformatics analysis of retinal ischemia/reperfusion injury in rats with potential key genes

Identification of key immune genes of osteoporosis based on bioinformatics and machine learning

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