PO.BCS01.17 · 生物信息与计算
多组学状态转换框架揭示急性髓系白血病中化疗诱导的代谢重编程
Multiomic state-transition framework reveals chemotherapy-induced metabolic reprogramming in acute myeloid leukemia
作者与单位 Authors & Affiliations
摘要 Abstract
中文摘要
急性髓系白血病(AML)是一种高度致死性的血液系统恶性肿瘤，长期生存率低于32%，这在很大程度上归因于由耐治疗白血病干细胞和代谢重编程驱动的疾病复发。这种不良预后凸显了对能够预测疾病动力学和治疗效果的框架的需求。我们此前应用状态转换理论，将AML疾病演变建模为mRNA和微小RNA(miRNA)转录组在各自AML状态空间中的轨迹，其特征为具有三个临界点的白血病发生潜能，分别代表健康、转换和白血病状态。在此，我们应用状态转换理论来表征化疗诱导的转录组和代谢变化，并检验以下假设：化疗通过促进向健康状态的转换同时诱导代谢转变，从而改变白血病发生潜能景观。使用CBFB::MYH11敲入小鼠AML模型，我们在一个由阿糖胞苷和柔红霉素组成的"5+3"化疗方案(模拟标准治疗"7+3"方案)前后每周采集外周血样本。所有血样均进行了bulk RNA-seq和miRNA-seq，我们使用PCA构建了基于mRNA和基于miRNA的状态空间。分析mRNA和miRNA转录组随时间的轨迹，我们观察到两种转录组轨迹在化疗后均向健康状态转换，但最终复发。值得注意的是，miRNA轨迹表现出治疗后2周或更长时间的延迟应答，揭示了mRNA-miRNA动力学的失同步。为评估代谢意义，我们进行了基因集变异分析(GSVA)。该分析揭示氧化磷酸化、糖酵解和脂肪酸代谢通路在缓解期下调，提示低代谢状态，但在复发期上调，与代谢重编程作为AML复发标志的观点一致。最后，我们将状态转换框架扩展至二维(2D)，以表征化疗如何影响mRNA-miRNA相互作用和多组学潜能景观。多组学状态空间揭示了AML进展期间强烈的mRNA-miRNA相关性，该相关性在化疗后被改变。此外，2D状态转换模型使我们能够捕捉化疗对多组学潜能的影响，并模拟mRNA-miRNA相互作用如何随时间变化。重要的是，模型模拟捕捉到了与mRNA和miRNA时间序列数据轨迹相似的应答后复发过程。总之，我们的发现表明，一个基于数学的多组学状态转换框架能够捕捉化疗诱导的转录组和代谢动力学，为预测应答和识别AML中的代谢脆弱性提供了一种系统方法。
查看英文原文 English abstract
Acute myeloid leukemia (AML) is a highly lethal hematological malignancy with a long-term survival below 32%, largely due to disease relapse driven by treatment-resistant leukemia stem cells and metabolic reprograming. This poor prognosis underscores the need for frameworks that can predict disease dynamics and treatment effects. We previously applied state-transition theory to model AML disease evolution as trajectories of the mRNA and microRNA (miRNA) transcriptomes in their respective AML state-space, characterized by a leukemogenic potential with three critical points representing health, transition, and leukemia states. Here, we apply state-transition theory to characterize chemotherapy-induced transcriptomic and metabolic changes and test the hypothesis that chemotherapy alters the leukemogenic potential landscape by promoting transitions towards health while inducing metabolic shifts. Using a CBFB::MYH11 knock-in murine model of AML, we collected weekly peripheral blood samples before and after a “5+3” chemotherapy regimen consisting of cytarabine and daunorubicin, modeling the standard-of-care “7+3” regimen. All blood samples were subjected to bulk RNA-seq and miRNA-seq and we used PCA to construct mRNA- and miRNA-based state-spaces. Analyzing the mRNA and miRNA transcriptome trajectories over time, we observed that both transcriptome trajectories transitioned towards a health state post-chemotherapy but ultimately relapsed. Notably, the miRNA trajectories exhibited a delayed response of 2 or more weeks after treatment, revealing desynchronized mRNA-miRNA dynamics. To assess metabolic implications, we performed a Gene Set Variation Analysis (GSVA). This analysis revealed that oxidative phosphorylation, glycolysis and fatty acid metabolism pathways were downregulated during remission, indicating a low metabolic state, but were upregulated during relapse, consistent with metabolic reprogramming as a hallmark of AML recurrence. Finally, we extended the state-transition framework to two-dimensions (2D) to characterize how chemotherapy affects the mRNA-miRNA interplay and multiomic potential landscape. The multiomic state-space revealed a strong mRNA-miRNA correlation during AML progression, which is altered after chemotherapy. Further, the 2D state-transition model enables us to capture the effects of chemotherapy on multiomic potential and simulate how the mRNA-miRNA interplay changes over time. Importantly, model simulations capture response followed by relapse similar to the mRNA and miRNA time-series data trajectories. Together, our findings demonstrate that a mathematically grounded, multiomic state-transition framework captures chemotherapy-induced transcriptomic and metabolic dynamics, offering a systematic approach to predict response and identify metabolic vulnerabilities in AML.
利益披露 Disclosure
J. Rangel Ambriz, None..
Z. Chen, None..
G. Marcucci, None.