Karner Heather, Mittmann Tabea, Chen Vicky W, Borah Ashir A, Langen Andreas, Yousefi Hassan, Fish Lisa, Zaro Balyn W, Navickas Albertas, Goodarzi Hani
Department of Biochemistry & Biophysics, University of California San Francisco, San Francisco, CA, USA.
Department of Urology, University of California San Francisco, San Francisco, CA, USA.
bioRxiv. 2025 Jun 7:2025.06.06.658309. doi: 10.1101/2025.06.06.658309.
Heterogeneity in cancer gene expression is typically linked to genetic and epigenetic alterations, yet post-transcriptional regulation likely influences these patterns as well. However, the quantitative contribution of post-transcriptional mechanisms to cancer transcriptome dynamics remains unclear. Here, we systematically measured mRNA dynamics across diverse breast cancer models, revealing that mRNA stability significantly shapes gene expression variability. To decipher the regulatory grammar underlying these dynamics, we developed GreyHound, an interpretable multimodal deep-learning framework integrating RNA sequence features and RNA-binding protein (RBP) expression. GreyHound identified an extensive network of RBPs and their regulons underlying variations in mRNA stability. Among these, we uncovered a metastasis-suppressive regulatory axis centered on the RNA-binding protein RBMS3 and its post-transcriptional control of the redox regulator TXNIP. Functional and molecular analyses revealed that RBMS3 depletion resulted in targeted transcript destabilization, which was associated with poor clinical outcomes and enhanced metastatic potential in xenograft models. Using in vivo epistasis studies, we confirmed that RBMS3 regulation of TXNIP mRNA stability drives this metastasis-suppressive program. These findings position the RBMS3-TXNIP regulatory axis as a key post-transcriptional mechanism in breast cancer and illustrate how interpretable models of RNA dynamics can uncover hidden regulatory programs in disease.
癌症基因表达的异质性通常与基因和表观遗传改变有关，但转录后调控可能也会影响这些模式。然而，转录后机制对癌症转录组动态变化的定量贡献仍不清楚。在此，我们系统地测量了多种乳腺癌模型中的mRNA动态变化，发现mRNA稳定性显著影响基因表达变异性。为了解析这些动态变化背后的调控规律，我们开发了GreyHound，这是一个整合RNA序列特征和RNA结合蛋白（RBP）表达的可解释多模态深度学习框架。GreyHound确定了一个广泛的RBP及其调控子网络，它们是mRNA稳定性变化的基础。其中，我们发现了一个以RNA结合蛋白RBMS3为中心的转移抑制调控轴，以及RBMS3对氧化还原调节因子TXNIP的转录后调控。功能和分子分析表明，RBMS3的缺失导致靶向转录本不稳定，这与临床预后不良和异种移植模型中转移潜能增强有关。通过体内上位性研究，我们证实RBMS3对TXNIP mRNA稳定性的调控驱动了这一转移抑制程序。这些发现将RBMS3-TXNIP调控轴定位为乳腺癌中的关键转录后机制，并说明了RNA动态变化的可解释模型如何揭示疾病中隐藏的调控程序。
