Halder Suvankar, Lawrence Michael C, Testa Giuliano, Periwal Vipul
bioRxiv. 2025 Feb 27:2025.02.21.639518. doi: 10.1101/2025.02.21.639518.
Liver resection initiates a meticulously coordinated hyperplasia process characterized by regulated cell proliferation that drives liver regeneration. This process concludes with the complete restoration of liver mass, showcasing the precision and robustness of this homeostasis. The remarkable capacity of the liver to regenerate rapidly into a fully functional organ has been crucial to the success of living donor liver transplantation (LDLT). In healthy livers, hepatocytes typically remain in a quiescent state (G0). However, following partial hepatectomy, these cells transition to the G1 phase to re-enter the cell cycle. Surgical resection induces various stresses, including physical injury, altered blood flow, and increased metabolic demands. These all trigger the activation and suppression of numerous genes involved in tissue repair, regeneration, and functional recovery. Both coding and noncoding RNAs detectable in the bloodstream during this process provide valuable insights into the gene responses driving liver recovery. This study integrates clinical gene expression data into a previously developed mathematical model of liver regeneration, which tracks transitions among quiescent, primed, and proliferating hepatocytes to construct virtual, patient-specific liver models. Using whole transcriptome RNA sequencing data from 12 healthy LDLT donors, collected at 14 time points over a year, we identified liver resection-specific gene expression patterns through Weighted Gene Co-expression Network Analysis (WGCNA). These patterns were organized into distinct clusters with unique transcriptional dynamics and mapped to model variables using deep learning techniques. Consequently, we developed a Personalized Progressive Mechanistic Digital Twin (PePMDT) for the livers of LDLT donors. The resulting PePMDT predicts individual patient recovery trajectories by leveraging blood-derived gene expression data to simulate regenerative responses. By transforming gene expression profiles into dynamic model variables, this approach bridges clinical data and mathematical modeling, providing a robust platform for personalized medicine. This study highlights the transformative potential of data-driven frameworks like PePMDT in advancing precision medicine and optimizing recovery outcomes for LDLT donors.
肝切除引发了一个精心协调的增生过程，其特征是受调控的细胞增殖驱动肝脏再生。这个过程以肝脏质量的完全恢复而告终，展示了这种内稳态的精确性和稳健性。肝脏迅速再生为一个功能完全正常的器官的非凡能力，对活体肝移植（LDLT）的成功至关重要。在健康肝脏中，肝细胞通常处于静止状态（G0期）。然而，部分肝切除术后，这些细胞会转变到G1期以重新进入细胞周期。手术切除会引发各种应激，包括物理损伤、血流改变和代谢需求增加。所有这些都会触发参与组织修复、再生和功能恢复的众多基因的激活和抑制。在此过程中，血液中可检测到的编码和非编码RNA为驱动肝脏恢复的基因反应提供了有价值的见解。本研究将临床基因表达数据整合到先前开发的肝脏再生数学模型中，该模型追踪静止、预激活和增殖肝细胞之间的转变，以构建虚拟的、患者特异性的肝脏模型。利用来自12名健康LDLT供体的全转录组RNA测序数据，这些数据在一年中的14个时间点收集，我们通过加权基因共表达网络分析（WGCNA）确定了肝切除特异性基因表达模式。这些模式被组织成具有独特转录动态的不同簇，并使用深度学习技术映射到模型变量。因此，我们为LDLT供体的肝脏开发了一个个性化渐进机制数字孪生模型（PePMDT）。由此产生的PePMDT通过利用血液来源的基因表达数据来模拟再生反应，预测个体患者的恢复轨迹。通过将基因表达谱转化为动态模型变量，这种方法架起了临床数据和数学建模之间的桥梁，为个性化医疗提供了一个强大的平台。这项研究突出了像PePMDT这样的数据驱动框架在推进精准医疗和优化LDLT供体恢复结果方面的变革潜力。
