Therapeutic rewiring of ceRNA networks: a computational pipeline for drug repurposing in acute kidney injury via circRNA-miRNA-mRNA axis disruption.

IF 4.1 3区 医学 Q2 GENETICS & HEREDITY
Ming Wang, Feng Gao
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引用次数: 0

Abstract

This review synthesizes current evidence on competing endogenous RNA (ceRNA) regulation in acute kidney injury (AKI) and proposes a practical, reproducible framework for computationally guided drug repurposing that aims to rewire pathogenic circRNA–miRNA–mRNA axes. We summarize the ceRNA concept and its biological rationale in renal injury, anchored to foundational descriptions of the ceRNA hypothesis. Building on curated circRNA resources and drug–gene mapping platforms, we present and critically evaluate a five-stage computational framework: (1) multi-omics data mining and ceRNA triplet inference, (2) network pharmacology mapping to drug–gene resources, (3) RNA-aware molecular docking to candidate structured RNA motifs, (4) molecular dynamics (MD) refinement and rescoring, and (5) integrative prioritization with explicit experimental filters for renal safety and tractability. We synthesize methodological best practices and limitations for each stage, with particular attention to the emerging field of RNA-targeted small molecules and their experimental validation. Three illustrative case studies demonstrate how distinct repurposing logics (direct RNA engagement, network/miRNA modulation, pathway-level modulation) can be mapped onto this pipeline and what orthogonal assays are required to progress candidates from in silico nomination to biochemical and in vivo testing. The review concludes with a pragmatic experimental roadmap, clear caveats (RNA structural dynamics, cellular context dependency, miRNA redundancy), and proposals to integrate single-cell and systems-pharmacology approaches to improve replicability and translational potential. By collating the literature and proposing a transparent, stepwise framework with validation benchmarks, this review aims to generate testable, prioritized in-silico hypotheses for RNA-aware repurposing in AKI that require rigorous biochemical and preclinical validation before any consideration of clinical testing. Because AKI arises from multiple, biologically distinct etiologies (for example ischemia–reperfusion injury, sepsis, nephrotoxic injury such as cisplatin exposure, and obstructive causes), candidate drugs and circRNA/miRNA targets must be evaluated in etiology-matched cellular and animal models; the pipeline therefore explicitly recommends etiology-aware filtering and model selection to avoid overgeneralizing findings across all AKI types.

ceRNA网络的治疗性重新布线:通过circRNA-miRNA-mRNA轴断裂在急性肾损伤中重新利用药物的计算管道。
本综述综合了目前关于急性肾损伤(AKI)中竞争性内源性RNA (ceRNA)调控的证据,并提出了一个实用的、可重复的计算指导药物重定向框架,旨在重新连接致病性circRNA-miRNA-mRNA轴。我们总结了ceRNA概念及其在肾损伤中的生物学原理,并以ceRNA假说的基本描述为基础。基于精心策划的环状RNA资源和药物基因定位平台,我们提出并批判性地评估了一个五阶段的计算框架:(1)多组学数据挖掘和ceRNA三重联体推断,(2)药物基因资源的网络药理学定位,(3)RNA感知分子对接候选结构化RNA基序,(4)分子动力学(MD)改进和重新评分,以及(5)结合肾脏安全性和可追溯性的明确实验过滤器进行综合优先排序。我们综合了每个阶段的方法最佳实践和局限性,特别关注rna靶向小分子的新兴领域及其实验验证。三个说明性案例研究展示了如何将不同的重新利用逻辑(直接RNA参与,网络/miRNA调制,途径水平调制)映射到该管道上,以及需要哪些正交试验来将候选物从硅提名推进到生化和体内测试。该综述总结了一个实用的实验路线图,明确的警告(RNA结构动力学,细胞环境依赖性,miRNA冗余),并提出了整合单细胞和系统药理学方法以提高可复制性和翻译潜力的建议。通过整理文献并提出一个透明的、具有验证基准的逐步框架,本综述旨在为AKI中rna感知再利用产生可测试的、优先的计算机假设,这些假设在考虑临床试验之前需要严格的生化和临床前验证。由于AKI由多种生物学上不同的病因引起(例如缺血再灌注损伤、败血症、顺铂暴露等肾毒性损伤和阻塞性病因),候选药物和circRNA/miRNA靶点必须在病因匹配的细胞和动物模型中进行评估;因此,管道明确推荐病因意识过滤和模型选择,以避免对所有AKI类型的结果进行过度概括。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Human Genomics
Human Genomics GENETICS & HEREDITY-
CiteScore
6.00
自引率
2.20%
发文量
55
审稿时长
11 weeks
期刊介绍: Human Genomics is a peer-reviewed, open access, online journal that focuses on the application of genomic analysis in all aspects of human health and disease, as well as genomic analysis of drug efficacy and safety, and comparative genomics. Topics covered by the journal include, but are not limited to: pharmacogenomics, genome-wide association studies, genome-wide sequencing, exome sequencing, next-generation deep-sequencing, functional genomics, epigenomics, translational genomics, expression profiling, proteomics, bioinformatics, animal models, statistical genetics, genetic epidemiology, human population genetics and comparative genomics.
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