Volume  4  Issue  3

封面解读

李建雄 / 王书缘

Radiation-induced lung injury (RILI) is a major dose-limiting complication of thoracic radiotherapy, and effective preventive strategies remain limited in clinical practice. Growing evidence suggests that host responses to radiation are influenced not only by local tissue damage but also by systemic signals originating from the gut microbiota, making these distant interactions of increasing translational interest. This study identifies a microbiota-dependent protective role of berberine in RILI. It further reveals a link between microbiota-derived metabolites and epigenetic regulation of lung injury responses. By modulating the gut microbial ecosystem, berberine increases the production of microbiota-derived inosine, which reaches the lung through the gut–lung axis and is associated with enhanced tissue resilience to radiation injury. The cover visualizes the gut–lung axis as a systemic pathway through which microbial metabolism shapes epigenetic responses and protects lung tissue during radiotherapy.

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All Papers

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1. Decoding influenza virus: From polymerase mechanisms to translational therapeutics

通信作者：邓涛、高福、Stephen Cusack、Mark von Itzstein、Ervin Fodor、Jonathan Grimes、Aartjan J.W. te Velthuis

Influenza viruses are a major cause of respiratory illness, with significant public health impact due to their ability to cause pandemics. This dialogue brought together experts including Professors George Fu Gao, Stephen Cusack, Mark von Itzstein, Ervin Fodor, Jonathan Grimes, Aartjan J.W. te Velthuis, and Tao Deng to decode the pressing scientific challenges and future directions in influenza research. They discussed how structural studies of the influenza polymerase have advanced our understanding of viral RNA transcription and replication. 

引用：

Deng T, Gao GF, Cusack S, et al. Decoding influenza virus: From polymerase mechanisms to translational therapeutics. hLife 2026; 4: 127–131.

2. Move without moving: Kidney-produced betaine mimics exercise to slow aging

hLife | Move without moving：肾脏来源甜菜碱可作为运动模拟物延缓衰老

通信作者：贺明

本文对一项新颖的多组学策略进行了点评与展望。该研究确定甜菜碱为关键的运动诱导分子，其通过抑制TBK1介导抗炎和延缓衰老，系统地描绘了急性运动与长期运动之间的分子差异，同时建立了肾脏代谢与系统性延缓衰老益处之间的直接联系。该研究系统地阐明了肾脏在运动介导的系统性健康效应中的核心作用，通过揭示代谢器官是系统性延缓衰老效应的核心协调者，重新定义了我们对运动生理学的理解，并将甜菜碱定位为开发模拟运动疗法的原型，开创了精准运动模拟物的研究框架。

Li Y, Tang X, Fu Y, et al. Move without moving: Kidney-produced betaine mimics exercise to slow aging. hLife 2026; 4: 132–134.

3. Diagnostics for human pathogenic fungal infections: Current status and future prospects

hLife | 精准诊断真菌感染：从技术突破到临床融合

通信作者：黄新华、陈昌斌

本文总结了真菌感染诊断领域的最新进展，并深入探讨相关技术在抗真菌耐药性分析中的关键作用，为实现临床精准治疗和改善患者预后提供了新的思路。

Huang X, Moses M, Nie L, et al. Diagnostics for human pathogenic fungal infections: Current status and future prospects. hLife 2026; 4: 135–164.

4. Berberine alleviates radiation-induced lung injury by promoting microbiota-derived inosine via the gut–lung axis

hLife | 解放军总医院李建雄团队研究揭示：黄连素通过肠–肺轴缓解放射性肺损伤

通信作者：李建雄

放疗是肺癌及多种胸部肿瘤的重要治疗手段，但肺组织对电离辐射高度敏感，放射性肺损伤是临床中最常见且最棘手的放疗并发症之一，严重影响患者生活质量。本研究从肠–肺轴视角出发，探讨了黄连素在放射性肺损伤中的作用，提示肠道菌群及其代谢变化可能参与放射应答的调控，为理解放射性肺损伤的调控过程提供了线索。未来仍需结合临床研究进一步验证这些发现，并探索其在放疗相关毒性评估与干预中的潜在价值。

Wang S, Huai S, Yuan Z, et al. Berberine alleviates radiation-induced lung injury by promoting microbiota-derived inosine via the gut–lung axis. hLife 2026; 4: 165–185.

5. Pan-prediction of major histocompatibility complex class II–restricted epitopes across species via an AlphaFold-based quantification scheme

hLife | AlphaFold量化方案：跨物种 MHC-II表位预测突破物种限制！

通信作者：王晟、徐实、杨汉春、张念之

本研究基于AlphaFold预测模型的性能，开发了一种基于结构的AF-pred（AlphaFold-prediction）表位预测工具。本研究证实了AF-pred在预测MHC-II限制性表位方面的可行性，以及其在跨物种泛化能力和结果可解释性上的优势。随着AF-pred的迭代优化，其预测准确性将进一步提升，进而为T细胞表位疫苗的设计与开发提供更强支持，为公共卫生防护奠定更坚实的基础。

Wang S, Kong L, Hu D, et al. Pan-prediction of major histocompatibility complex class II–restricted epitopes across species via an AlphaFold-based quantification scheme. hLife 2026; 4: 186–204.

6. Risk prediction model of postoperative infection after transplantation

hLife | 上海交大医学院刘宁宁团队构建器官移植术后感染风险预测模型

通信作者：傅志仁、钟林、陈刚、赵杰、杨家印、郭文治、郑黎强、刘宁宁

Gao Q, Wu Y, Peng R, et al. Risk prediction model of postoperative infection after transplantation. hLife 2026; 4: 205–208.