智能体AI揭示跨组织保守蛋白质共丰度程序，这类程序是单数据集分析无法获取的
Agentic AI uncovers conserved cross-tissue protein co-abundance programs inaccessible to single-dataset analysis
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- School of Information Sciences, University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校信息科学学院）
- Department of Ecology and Evolutionary Biology, University of Michigan（密歇根大学生态与进化生物学系）
机构由 AI 辅助整理，请以论文原文为准。
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中文总结 AI 辅助
该研究提出LLM智能体框架，分析41种人体组织与体液的820种成对组合，识别出大量跨组织保守蛋白质共丰度簇，为疾病机制与治疗探索提供资源。
中文摘要 AI 辅助
跨组织保守的蛋白质共丰度簇可揭示共享疾病机制与候选治疗靶点，尤其是当器官特异性疾病相关蛋白在外周或可及组织中汇聚时。然而，以往的跨组织研究聚焦于生物学预先选定的组织对，多数可能的组合与非显而易见的关联未被探索。我们提出一种LLM智能体框架，用于大规模、基于证据的组织特异性蛋白质共丰度网络比较。该框架构建组织网络，推导成对共识簇，并整合表达图谱、蛋白质相互作用与复合物数据库、通路注释、疾病目录及文献的证据。将其应用于41种人体组织与体液的所有820种成对组合，共识别出406个组织对中的1833个保守共丰度簇。结肠、滑液、血液、脑脊液与骨髓是连接最广泛的组织，而簇最丰富的组织对以骨髓为主。分析还凸显了非显而易见的关联：皮肤-骨髓对的重要性超过解剖学相邻的骨-骨髓对，而结肠-乳腺对包含与癌症相关的簇，涉及细胞外基质重塑、脂质代谢与免疫调节。簇级分析产生了进一步的机制假设，包括脑-肠细胞外囊泡/氧化还原/5-羟色胺辅因子轴，以及涉及与白质疾病相关基因的肝-骨髓应激反应轴。这些结果提供了保守蛋白质共丰度的全局可比图谱，以及用于机制与治疗探索的假设生成资源。代码与数据可在该https URL获取。
英文摘要
Protein co-abundance clusters preserved across tissues can reveal shared disease mechanisms and candidate therapeutic targets, particularly when proteins implicated in organ-confined diseases converge in peripheral or accessible tissues. However, previous cross-tissue studies have focused on biologically pre-selected tissue pairs, leaving most possible combinations and non-obvious relationships unexplored. We present an LLM-agent framework for large-scale, evidence-grounded comparison of tissue-specific protein co-abundance networks. The framework constructs tissue networks, derives pairwise consensus clusters, and integrates evidence from expression atlases, protein interaction and complex databases, pathway annotations, disease catalogues, and literature. Applied to all 820 pairwise combinations of 41 human tissues and fluids, it identified 1,833 conserved co-abundance clusters across 406 tissue pairs. Colon, synovial fluid, blood, cerebrospinal fluid, and bone marrow were the most broadly connected tissues, while the most cluster-rich pairs were dominated by bone marrow. The analysis also highlighted non-obvious relationships: skin-bone marrow exceeded the anatomically adjacent bone-bone marrow pair, while colon-breast contained cancer-relevant clusters involving extracellular-matrix remodeling, lipid metabolism, and immune modulation. Cluster-level analyses generated further mechanistic hypotheses, including a brain-gut extracellular-vesicle/redox/serotonin-cofactor axis and a liver-bone marrow stress-response axis involving genes linked to white matter disease. These results provide a global, comparable landscape of conserved protein co-abundance and a hypothesis-generating resource for mechanistic and therapeutic exploration. Code and data are available at https://github.com/Gry1005/AgenticAI-conserved-cross-tissue-protein-co-abundance.
