AI揭示太空失重让雌鼠脂肪产热基因提升12倍，或助航天与代谢病研究
Explainable Machine Learning Reveals 12-Fold Ucp1 Upregulation and Thermogenic Reprogramming in Female Mouse White Adipose Tissue After 37 Days of Microgravity: First AI/ML Analysis of NASA OSD-970
- 用可解释机器学习分析太空实验数据，发现失重下脂肪组织基因变化
- Ucp1基因表达升12.21倍，热生成通路整体激活3.24倍，显著差异
- 首次用AI解析NASA公开太空生物学数据，适合空间医学与代谢研究者
微重力会引发哺乳动物生理的深刻代谢适应，但女性白色脂肪组织（WAT）中产热的分子机制仍不清楚。本文首次对美国宇航局开放科学数据仓库（OSDR）数据集OSD-970（源自鼠类研究-1任务）进行机器学习分析。基于16只雌性C57BL/6J小鼠（8只飞行组，8只地面对照组）在国际空间站停留37天后腹股沟WAT中89个脂肪生成与产热通路基因的RT-qPCR数据，采用差异表达分析、多种机器学习分类器及留一法交叉验证（LOO-CV），并结合可解释人工智能（SHAP）方法。最显著发现为微重力暴露下Ucp1基因表达上调12.21倍（ΔΔCt = -3.61，p = 0.0167），伴随产热通路整体激活（平均通路倍数变化=3.24）。表现最佳模型（随机森林，前20个特征）在LOO-CV下达AUC=0.922，准确率=0.812，F1=0.824。SHAP分析一致将Ucp1列为关键预测特征，而Angpt2、Irs2、Jun和Klf家族转录因子为共性主导特征。主成分分析（PCA）显示飞行组与对照组明显分离，第一主成分解释69.1%方差。结果表明女性小鼠脂肪组织在微重力下出现快速产热重编程，可能为代偿反应。本研究展示了可解释AI在重新分析新发布的NASA空间生物学数据中的潜力，对长期载人航天中女性宇航员健康及地球上的肥胖与代谢疾病研究具有直接意义。
原文摘要 · Abstract (English)
Microgravity induces profound metabolic adaptations in mammalian physiology, yet the molecular mechanisms governing thermogenesis in female white adipose tissue (WAT) remain poorly characterized. This paper presents the first machine learning (ML) analysis of NASA Open Science Data Repository (OSDR) dataset OSD-970, derived from the Rodent Research-1 (RR-1) mission. Using RT-qPCR data from 89 adipogenesis and thermogenesis pathway genes in gonadal WAT of 16 female C57BL/6J mice (8 flight, 8 ground control) following 37 days aboard the International Space Station (ISS), we applied differential expression analysis, multiple ML classifiers with Leave-One-Out Cross-Validation (LOO-CV), and Explainable AI via SHapley Additive exPlanations (SHAP). The most striking finding is a dramatic 12.21-fold upregulation of Ucp1 (Delta-Delta-Ct = -3.61, p = 0.0167) in microgravity-exposed WAT, accompanied by significant activation of the thermogenesis pathway (mean pathway fold-change = 3.24). The best-performing model (Random Forest with top-20 features) achieved AUC = 0.922, Accuracy = 0.812, and F1 = 0.824 via LOO-CV. SHAP analysis consistently ranked Ucp1 among the top predictive features, while Angpt2, Irs2, Jun, and Klf-family transcription factors emerged as dominant consensus classifiers. Principal component analysis (PCA) revealed clear separation between flight and ground samples, with PC1 explaining 69.1% of variance. These results suggest rapid thermogenic reprogramming in female WAT as a compensatory response to microgravity. This study demonstrates the power of explainable AI for re-analysis of newly released NASA space biology datasets, with direct implications for female astronaut health on long-duration missions and for Earth-based obesity and metabolic disease research.
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