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<p>MicroRNAs (miRNAs) bind to the 3′ untranslated region of mRNA transcripts, exerting inhibitory activity over gene expression. RNA-binding proteins (RBPs) involved in post-transcriptional regulation also play a pivotal role in modulating mRNA, exhibiting similar binding and regulatory effects to mRNA as miRNAs. This convergence raises the intriguing possibility of coordinated or competitive regulation between miRNAs and RBPs when targeting the common mRNA. However, accurately quantifying the complex regulatory relationship between miRNAs and RBPs remains a challenge. To address this challenge, we here propose a novel multivariate information-based approach to quantitatively capture the nonlinear regulatory relationships between miRNAs and RBPs in the context of shared mRNA targets. Our method integrates the sequence information with gene expression data, unveiling a comprehensive perspective on such an intricate regulatory network. Our findings reveal a prevalent synergistic relationship between miRNAs and RBPs, surpassing instances of competitive relationship. This innovative approach enhances our understanding of the complex interplay between miRNAs and RBPs, shedding light on the cooperative mechanisms that drive post-transcriptional regulation.</p>

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Utilizing partial information decomposition to evaluate the complex interplay between microRNA and RNA-binding protein in regulating the shared target mRNA

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年9月26日

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  • Tianwu Zhang,
  • Yanlin Chen,
  • Wenrong Wang,
  • Chu Pan

摘要

MicroRNAs (miRNAs) bind to the 3′ untranslated region of mRNA transcripts, exerting inhibitory activity over gene expression. RNA-binding proteins (RBPs) involved in post-transcriptional regulation also play a pivotal role in modulating mRNA, exhibiting similar binding and regulatory effects to mRNA as miRNAs. This convergence raises the intriguing possibility of coordinated or competitive regulation between miRNAs and RBPs when targeting the common mRNA. However, accurately quantifying the complex regulatory relationship between miRNAs and RBPs remains a challenge. To address this challenge, we here propose a novel multivariate information-based approach to quantitatively capture the nonlinear regulatory relationships between miRNAs and RBPs in the context of shared mRNA targets. Our method integrates the sequence information with gene expression data, unveiling a comprehensive perspective on such an intricate regulatory network. Our findings reveal a prevalent synergistic relationship between miRNAs and RBPs, surpassing instances of competitive relationship. This innovative approach enhances our understanding of the complex interplay between miRNAs and RBPs, shedding light on the cooperative mechanisms that drive post-transcriptional regulation.