Research Article

孟德尔随机转录组学与网络药理学用于骨关节炎药物靶点的识别

DOI:

10.3791/69569

⸱

January 23rd, 2026

 ,  ,  ,  ,  ,  ,  ,  ,  ,  ,  , 
1Smart minimally invasive orthopedics and interventional Medicine department, Nanchang Hongdu Hospital of Traditional Chinese Medicine, 2Nanchang Hongdu Hospital of Traditional Chinese Medicine, 3Jiangxi University of Chinese Medicine, 4Department of Orthopedic Hospital, the 1st Affiliated Hospital, Jiangxi Medical College, Nanchang University

In This Article

Summary

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我们提出了一种方法学方案，整合了孟德尔随机化、转录组分析和网络药理学，系统地识别骨关节炎的候选药物靶点。该方法通过遗传、表达和相互作用证据，利用公开数据和计算工具，优先排序靶点。

Abstract

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本文阐述了一种综合方法，结合孟德尔随机化（MR）、转录组分析和网络药理学，以识别并优先确定骨关节炎（OA）潜在治疗靶点。该研究旨在指导研究人员实施这一多模态流程，以研究药物再利用及OA新疗法干预的开发。该方法框架包括五个顺序阶段：首先是磁共振分析流程，采用双样本磁共振识别与骨关节炎相关的假定因果血浆蛋白，随后进行斯泰格滤波和全表组关联扫描，以评估因果方向性和潜在的非靶点效应;第二，转录组测序工作流程，整合RNA测序数据以识别和优化候选蛋白靶点;第三，整合策略，将磁共振和转录组结果合并以优先选择候选蛋白;第四，网络药理学和分子结合程序，涉及构建蛋白质-蛋白质相互作用网络、功能富集分析和分子结合以探索配体-靶点相互作用;第五，预期应用，重点关注具有潜在治疗意义的候选化合物和天然产物的优先排序。通过将工作流程组织为不同的分析阶段，该框架提供了一种可重复的方法，用于从遗传和转录组发现过渡到计算药物靶向评估，而无需呈现具体的实验结果。该方法促进了利用公开数据集和计算工具对OA治疗的系统性和假设驱动的研究。

Introduction

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骨关节炎（OA）的特征是软骨逐渐退化，软骨下骨骼发生变化，以及骨骼过度生长1。如今，它已成为全球致残和经济负担的主要原因之一。目前的治疗主要包括类固醇或非甾体抗炎药（NSAIDs），以缓解疼痛和炎症。然而，NSAIDs常常在胃肠道和心血管系统中引发不良副作用3。关节置换手术提供了另一种选择，但约三分之一的患者并未经历显著的疼痛缓解或功能改善4.随着我们对骨关节炎分子机制理解的深入，出现了多个潜在的治疗靶点，使得更精准和个性化的治疗成为可能5.鉴于人类蛋白质在生物过程中的关键作用，它们是OA药物开发的关键候选者。Nelson等人提出，选择基因支持的靶点可能有助于解决临床开发中药物的高失败率问题6。

孟德尔随机化（MR）是一种遗传学方法，通过使用全基因组关联研究（GWAS）中的单核苷酸多态性（SNP）作为工具变量，推断暴露与结局之间的因果关系 7,8。与观察性研究相比，MR利用受孕时基因型的随机组合来减少混杂并逆转因果关系9。敏感性分析有助于解释多基因多效性，提供近似因果关系的推断。凭借这些优势，磁共振越来越多地用于识别多发性硬化症和类风湿性关节炎等疾病的治疗靶点。然而，基于MR的预测仍需实验验证，这可以通过转录组分析来优先识别关键疾病相关基因。

网络药理学是一种结合系统生物学和计算工具的跨学科方法，提出疾病往往是由生物网络的破坏引起，而非单基因突变。该方法通过挖掘药物-疾病数据库，实现多组分药物的分析及其治疗靶点的系统性预测13。分子对接是该框架的关键组成部分，模拟药物与靶点之间的结合亲和力，以评估药物的可药性。尽管取得了这些进展，但很少有研究将磁共振与网络药理学结合治疗OA。

本文提出了一种结合磁共振、转录组分析和网络药理学的方案，以识别血浆蛋白作为OA可行治疗靶点。其目标不仅是识别致病蛋白，还通过计算分析优先排序候选治疗化合物。

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Protocol

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我们获得了南昌大学第一附属医院生物医学研究伦理委员会的伦理审批和知情同意。伦理编号：（2025）CDYFYYLK（08-007）。

磁共振分析

数据检索
血浆pQTL数据来自Zheng等人14的研究，整合了五个GWAS数据集15、16、17、18、19，以及Ferkingstad等人的研究。数据纳入标准如下：（i） 全基因组显著关联（p < 5 × 10⁻⁸）;以及（ii）血浆蛋白作为OA潜在治疗靶点。研究设计总结见 图1。首先，我们利用IEU OpenGWAS的GWAS数据和Zheng14 和Ferkingstad20（补充表S1 和补充 表S2）的研究中的血浆pQTL数据确定了候选治疗靶点。随后进行了斯泰格滤波和表型扫描以验证结果的稳健性。IEU OpenGWAS（https://gwas.mrcieu.ac.uk/）被用于获取髋关节骨关节（n = 417,596）、膝关节骨关节（n = 403,124）和髋关节骨关节（n = 393,873）的汇总统计数据21。

SNP过滤命令

具有全基因组显著性的SNP（p < 5 × 10⁻⁸）在MR分析前接受了聚团过程（r² < 0.001，F统计量> 10，窗口大小=10,000 kb）。

磁共振分析

为研究潜在药物靶点，采用血浆蛋白作为暴露，OA作为结果，通过R（v4.3.1）中的“TwoSampleMR”软件包实现磁共振分析。当蛋白质仅有单一pQTL时，采用Wald比率;否则，采用逆方差加权MR（MR-IVW），随后进行异质性和多效性评估。采用Bonferroni校正以考虑多重测试，蛋白优先级阈值为p < 5.63×10⁻⁵。

斯泰格滤波与表型扫描

为了评估反向因果关系，我们进行了斯泰格滤波。当p为0.05时，“TRUE”结果<表示无反向因果关系。使用LDtrait （https://ldlink.nih.gov/?tab=ldtrait#home-tab）22 进行表型扫描，以考察pQTLs与其他性状的关联。阈值为R² = 0.1，以及±500,000碱基对窗口。多效性效应被赋予满足以下两项条件的pQTLs：（i）全基因组显著关联（p < 5 × 10⁻⁸），以及（ii）与已知骨关节炎风险因子的关联。

现象范围关联研究

为考虑基因多效性和非靶点效应，我们使用阿斯利康PheWAS门户（https://azphewas.com/）开展了一项现象级关联研究（PheWAS），该门户包含来自约45万名英国生物样本库参与者的15,500个二元表型和1,500个连续表型23。阈值被设置为默认值以减少误报。

蛋白质-蛋白质相互作用（PPI）网络

为了可视化MR识别的潜在蛋白靶点之间的相互作用，我们使用GeneMANIA（https://genemania.org/）进行蛋白质间相互作用分析和结果可视化24。

浓缩分析

为了探究生物学相关性，我们利用 https://www.bioinformatics.com.cn 的生物信息学工具进行了富集分析，进行数据分析和可视化。

转录组工作流程

按照制造商的指导，使用RNA提取试剂套装提取了总RNA。RNA质量通过自动RNA质量评估系统进行评估;仅使用含有RIN ≥7.0的样本。通过无RNase琼脂糖凝胶电泳（1.5%凝胶）确认了质量。真核mRNA通过寡核苷酸（dT）珠子进行富集;原核mRNA通过RNA消除磁性试剂盒进行了富集。mRNA被片段化（200-700 nt），并利用RNA文库制备套件转化为cDNA。cDNA文库经过末端修复、A尾处理、连接接头、使用DNA纯化磁珠（1.0×）纯化，并通过PCR扩增。测序是在高通量的下一代测序平台上进行的。差异表达基因由log₂FC >1定义，并调整p <0.05。

网络药理学

为了识别潜在的靶蛋白药物，我们使用了BATMAN-TCM （http://bionet.ncpsb.org.cn/batman-tcm/index.php）25。评分门槛为0.74（LR = 32.5）用于选择已知和预测化合物。从TCMSP（https://old.tcmsp-e.com/index.php）中提取草本成分，并用OB过滤30%>DL过滤>0.1826。

分子对接

分子对接用于评估结合相互作用。从PDB中提取了蛋白质结构（https://www.rcsb.org/）。UCSF Chimera用于通过去除配体和溶剂对结构进行预处理。AutoDock 工具用于计算 Gasteiger 电荷并定义箱体中心和尺寸。药物结构取自PubChem（https://pubchem.ncbi.nlm.nih.gov/），并以类似方式进行预处理。对接通过AutoDock Vina完成。盒子尺寸因目标而异。结合亲和力被计算，结果在UCSF Chimera中可视化。

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Results

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筛查蛋白质组以筛查骨关节炎致病蛋白

利用Zheng等人研究中的血浆pQTL数据，孟德尔随机化（MR）分析发现了6个蛋白-膝关节或髋关节骨关节关联、1个蛋白-膝关节骨关节关联和4个蛋白-髋关节骨关节关联，均达到Bonferroni显著阈值（P < 5.63 × 10⁻⁵）（见表1和图2A-C）。含EGF的腓蛋白样细胞外基质蛋白1（EFEMP1）血浆水平升高（OR = 0.83;95% CI，0.77-0.90;P = 3.35 × 10⁻⁶），神经内分泌转化酶1（PCSK1）（OR = 0.96;95% CI，0.95-0.98;P = 2.35 × 10⁻⁶），含富含亮氨酸重复蛋白2的免疫球蛋白超家族（ISLR2）（OR = 0.81;95% CI，0....

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Discussion

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这是一种两样本的MR分析方法，已被用于预测与骨关节炎（OA）相关的潜在蛋白质，辅以测序、验证骨关节炎患者血液样本和网络药理学，以预测OA的药物靶点。我们利用血浆蛋白质组学数据和OA的GWAS数据进行双样本MR分析，识别出19种与OA相关的蛋白，包括EFEMP1、PTHLH、PCSK1、ECM1、ISLR2、SEMA3G、SPOCK2、LRIG3、TMEM190、CRYZ、KNG1、OMG、DDX19A、MAPK3、PTPN9、USP8、DDX19B、PROC和ITIH1。为验证MR结果的方向，还实施了Steiger滤波分析。随后，进行了表型扫描，排除了四种可能通过其他途径影响骨关节炎的蛋白质（EFEMP1、PTHLH、PCSK1、ECM1）。此外，采用pheWAS分析评估治疗性蛋白对其他性状是否有益或不利，并探讨任何未测量的潜在多效性效应，解决MR分析的局限性。同时，对骨关节炎患者外周血进行了差异表达基因转录组测序，发现31个上调基因和4557个下调基因。交叉分析与MR结果发现了五种靶蛋白（CRYZ、KNG1、USP8、DDX19A、DDX19B）。为了更好地理解这些蛋...

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Disclosures

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作者之间没有相互竞争的利益需要声明。

Acknowledgements

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该项目得到了江西省卫生委科技计划（202610930）和江西中药管理局科技计划（2025022398）的支持。

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Materials

List of materials used in this article
NameCompanyCatalog NumberComments
安捷伦2100生物分析仪安捷伦科技G2939AA自动化RNA质量评估系统
AutoDock Vina斯克里普斯研究所Vina 1.1.2用于计算分子对接的开源命令行软件
Illumina NovaSeq 6000 平台光辉SY-415-1001高通量下一代测序平台
NEBNext Ultra RNA 文库制备套件新英格兰生物实验室NEB #E7770将富集的mRNA片段并转录为cDNA
Ribo-Zero磁性套件震中MRZH11124消除rRNA
Trizol 试剂套件InvitrogenAM9738提取总RNA
加州大学旧金山分校奇美拉加州大学旧金山分校奇美拉-阿尔法-win64分子可视化与结构分析软件。

References

$$\rightleftharpoonup{xx}$$ $$\longleftharp{xx}$$, $$\longrightharp{xx}$$,
  1. Sirong, S., et al. Effects of tetrahedral framework nucleic acid/wogonin complexes on osteoarthritis. Bone Res. 8 (6), 6(2020).
  2. Glyn-Jones, S., et al. Osteoarthritis. Lancet. 386 (9991), 376-387 (2015).
  3. Bindu, S., Mazumder, S., Bandyopadhyay, U. Non-steroidal anti-inflammatory drugs (NSAIDs) and organ damage: a current perspective. Biochem Pharmacol. 180, 114147(2020).
  4. Costello, C. A., et al. Association between epidemiological factors and nonresponders to total joint replacement surgery in primary osteoarthritis patients. J Arthroplasty. 36 (5), 1502-1510.e5 (2021).
  5. Cho, Y., et al. Disease-modifying therapeutic strategies in osteoarthritis: current status and future directions. Exp Mol Med. 53 (11), 1689-1696 (2021).
  6. Nelson, M. R., et al. The support of human genetic evidence for approved drug indications. Nat Genet. 47 (8), 856-860 (2015).
  7. Reay, W. R., Cairns, M. J. Advancing the use of genome-wide association studies for drug repurposing. Nat Rev Genet. 22 (10), 658-671 (2021).
  8. Smith, G. D., Ebrahim, S. Mendelian randomization: can genetic epidemiology contribute to understanding environmental determinants of disease. Int J Epidemiol. 32 (1), 1-22 (2003).
  9. Lin, J., Zhou, J., Xu, Y. Potential drug targets for multiple sclerosis identified through Mendelian randomization analysis. Brain. 146 (8), 3364-3372 (2023).
  10. Cao, Y., Yang, Y., Hu, Q., Wei, G. Identification of potential drug targets for rheumatoid arthritis from genetic insights: a Mendelian randomization study. J Transl Med. 21 (1), 616(2023).
  11. Ju, S., et al. Potential therapeutic drug targets and pathways prediction for premature ovarian insufficiency based on network pharmacologic method. J Ethnopharmacol. 304, 116054(2023).
  12. Hopkins, A. L. Network pharmacology: the next paradigm in drug discovery. Nat Chem Biol. 4 (11), 682-690 (2008).
  13. Liu, X., et al. Integrating hippocampal metabolomics and network pharmacology deciphers the antidepressant mechanisms of Xiaoyaosan. J Ethnopharmacol. 268, 113549(2021).
  14. Zheng, J., et al. Phenome-wide Mendelian randomization mapping the influence of the plasma proteome on complex diseases. Nat Genet. 52 (10), 1122-1131 (2020).
  15. Suhre, K., et al. Connecting genetic risk to disease end points through the human blood plasma proteome. Nat Commun. 8, 14357(2017).
  16. Sun, B. B., et al. Genomic atlas of the human plasma proteome. Nature. 558 (7708), 73-79 (2018).
  17. Yao, C., et al. Genome-wide mapping of plasma protein QTLs identifies putatively causal genes and pathways for cardiovascular disease. Nat Commun. 9 (1), 3268(2018).
  18. Emilsson, V., et al. Co-regulatory networks of human serum proteins link genetics to disease. Science. 361 (6404), 769-773 (2018).
  19. Folkersen, L., et al. Mapping of 79 loci for 83 plasma protein biomarkers in cardiovascular disease. PLoS Genet. 13 (4), e1006706(2017).
  20. Ferkingstad, E., et al. Large-scale integration of the plasma proteome with genetics and disease. Nat Genet. 53 (12), 1712-1721 (2021).
  21. Tachmazidou, I., et al. Identification of new therapeutic targets for osteoarthritis through genome-wide analyses of UK Biobank data. Nat Genet. 51 (2), 230-236 (2019).
  22. Lin, S. H., Brown, D. W., Machiela, M. J. LDtrait: an online tool for identifying published phenotype associations in linkage disequilibrium. Cancer Res. 80 (16), 3443-3446 (2020).
  23. Wang, Q., et al. Rare variant contribution to human disease in 281,104 UK Biobank exomes. Nature. 597 (7877), 527-532 (2021).
  24. Warde-Farley, D., et al. The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function. Nucleic Acids Res. 38 (Web Server issue), W214-W220 (2010).
  25. Kong, X., et al. BATMAN-TCM 2.0: an enhanced integrative database for known and predicted interactions between traditional Chinese medicine ingredients and target proteins. Nucleic Acids Res. 51 (Database issue), D1268-D1276 (2023).
  26. Liu, H., Wang, J., Zhou, W., Wang, Y., Yang, L. Systems approaches and polypharmacology for drug discovery from herbal medicines: an example using licorice. J Ethnopharmacol. 146 (3), 773-793 (2013).
  27. Collins, K. H., et al. Adipose tissue is a critical regulator of osteoarthritis. Proc Natl Acad Sci U S A. 118 (1), (2021).
  28. Godziuk, K., Hawker, G. A. Obesity and body mass index: past and future considerations in osteoarthritis research. Osteoarthritis Cartilage. 32 (4), 452-459 (2024).
  29. Kohno, H., et al. Synovial fluids from patients with osteoarthritis and rheumatoid arthritis contain high levels of parathyroid hormone-related peptide. J Bone Miner Res. 12 (5), 847-854 (1997).
  30. Binvignat, M., Sellam, J., Berenbaum, F., Felson, D. T. The role of obesity and adipose tissue dysfunction in osteoarthritis pain. Nat Rev Rheumatol. 20, (2024).
  31. D'Angelo, D., De Martino, M., Arra, C., Fusco, A. Emerging role of USP8, HMGA, and non-coding RNAs in pituitary tumorigenesis. Cancers (Basel). 11 (9), (2019).
  32. Islam, M. T., Chen, F., Chen, H. The oncogenic role of ubiquitin-specific peptidase USP8 and its signaling pathways targeting for cancer therapeutics. Arch Biochem Biophys. 701, 108811(2021).
  33. Hunter, D. J., Bierma-Zeinstra, S. Osteoarthritis. Lancet. 393 (10182), 1745-1759 (2019).
  34. Den Hollander, W., et al. Knee and hip articular cartilage have distinct epigenomic landscapes: implications for future cartilage regeneration approaches. Ann Rheum Dis. 73 (12), 2208-2212 (2014).
  35. Han, J., et al. Discovery of novel USP8 inhibitors via ubiquitin-rhodamine-110 fluorometric assay-based high-throughput screening. Bioorg Chem. 101, 103962(2020).
  36. Martineau-Doizé, B., Lai, W. H., Warshawsky, H., Bergeron, J. J. In vivo demonstration of cell types in bone that harbor epidermal growth factor receptors. Endocrinology. 123 (2), 841-858 (1988).
  37. Qin, L., Beier, F. EGFR signaling: friend or foe for cartilage. JBMR Plus. 3 (2), e10177(2019).
  38. Konttinen, Y. T., et al. Acidic cysteine endoproteinase cathepsin K in the degeneration of the superficial articular hyaline cartilage in osteoarthritis. Arthritis Rheum. 46 (4), 953-960 (2002).
  39. Lecaille, F., Kaleta, J., Brömme, D. Human and parasitic papain-like cysteine proteases: their role in physiology and pathology and recent developments in inhibitor design. Chem Rev. 102 (12), 4459-4488 (2002).
  40. Huberts, D. H., Van Der Klei, I. J. Moonlighting proteins: an intriguing mode of multitasking. Biochim Biophys Acta. 1803 (4), 520-525 (2010).
  41. Piatigorsky, J. Gene sharing, lens crystallins and speculations on an eye/ear evolutionary relationship. Integr Comp Biol. 43 (4), 492-499 (2003).
  42. Schiavone, N., et al. A conserved AU-rich element in the 3′ untranslated region of BCL-2 mRNA is endowed with a destabilizing function involved in BCL-2 down-regulation during apoptosis. FASEB J. 14 (1), 174-184 (2000).
  43. Papucci, L., et al. Impact of targeting the adenine- and uracil-rich element of BCL-2 mRNA with oligoribonucleotides on apoptosis, cell cycle, and neuronal differentiation in SH-SY5Y cells. Mol Pharmacol. 73 (2), 498-508 (2008).
  44. Banerjee, P., Markande, S., Kalarikkal, M., Joseph, J. Sumoylation modulates the function of DDX19 in mRNA export. J Cell Sci. 135 (4), (2022).
  45. Primorac, D., Stover, M. L., Clark, S. H., Rowe, D. W. Molecular basis of nanomelia, a heritable chondrodystrophy of chicken. Matrix Biol. 14 (4), 297-305 (1994).
  46. Styrkarsdottir, U., et al. Whole-genome sequencing identifies rare genotypes in COMP and CHADL associated with high risk of hip osteoarthritis. Nat Genet. 49 (5), 801-805 (2017).
  47. Kashima, I., et al. Binding of a novel SMG-1-UPF1-eRF1-eRF3 complex to the exon junction complex triggers UPF1 phosphorylation and nonsense-mediated mRNA decay. Genes Dev. 20 (3), 355-367 (2006).
  48. Yamashita, A., et al. SMG-8 and SMG-9, two novel subunits of the SMG-1 complex, regulate remodeling of the mRNA surveillance complex during nonsense-mediated mRNA decay. Genes Dev. 23 (9), 1091-1105 (2009).
  49. Hug, N., Cáceres, J. F. The RNA helicase DHX34 activates NMD by promoting a transition from the surveillance to the decay-inducing complex. Cell Rep. 8 (6), 1845-1856 (2014).
  50. Yamashita, A., Ohnishi, T., Kashima, I., Taya, Y., Ohno, S. Human SMG-1 associates with components of the mRNA surveillance complex and regulates nonsense-mediated mRNA decay. Genes Dev. 15 (17), 2215-2228 (2001).
  51. Campbell, D. J. The kallikrein-kinin system in humans. Clin Exp Pharmacol Physiol. 28 (12), 1060-1065 (2001).
  52. Bhoola, K. D., Elson, C. J., Dieppe, P. A. Kinins: key mediators in inflammatory arthritis. Br J Rheumatol. 31 (8), 509-518 (1992).
  53. Mateos, J., et al. Differential protein profiling of synovial fluid from rheumatoid arthritis and osteoarthritis patients using LC-MALDI TOF/TOF. J Proteomics. 75 (10), 2869-2878 (2012).
  54. Pelletier, J. P., et al. Inhibition of subchondral bone resorption in early experimental dog osteoarthritis by licofelone is associated with reduced synthesis of MMP-13 and cathepsin K. Bone. 34 (3), 527-538 (2004).
  55. Vasiljeva, O., et al. Emerging roles of cysteine cathepsins in disease and their potential as drug targets. Curr Pharm Des. 13 (4), 387-403 (2007).
  56. Zeng, Z., et al. Pelargonidin ameliorates inflammatory response and cartilage degeneration in osteoarthritis via suppressing the NF-κB pathway. Arch Biochem Biophys. 743, 109668(2023).

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Mendelian RandomizationTranscriptomic AnalysisNetwork PharmacologyOsteoarthritis Drug TargetsTwo Sample MRRNA SequencingProtein Protein InteractionFunctional EnrichmentMolecular DockingDrug Repurposing

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