摘要
【目的】分析不同网络构建算法对根际微生物互作网络结构及关键类群识别的差异特征,阐明各类算法在构建微生物互作关系、挖掘关键类群方面的特点及优势,为网络构建方法的选择提供理论参考。【方法】以珍稀植物米槁的根际微生物群落为研究对象,整合成分稀疏相关分析(sparse correlations for compositional data,SparCC)、随机矩阵理论(random matrix theory,RMT)及共现网络(co-occurrence network,CoNet)这3种主流算法构建分子生态网络;结合PICRUSt2功能预测与关键类群-环境因子关联分析,多维度比较不同算法的网络结构特征与关键类群识别结果。【结果】网络构建算法显著影响网络结构表现:SparCC具有高模块化特性,相对模块化指数(relative modularity index,RM)为1.31,且互作隔离明显(边连通性=0);RMT则形成单一模块化结构(RM=0.78),连接呈均质化(接近中心化指数为0.22);CoNet因整合了26.0%的负相关边,导致模块化降低(RM=0.95),网络直径扩大至33.22个节点步长且鲁棒性下降。关键类群识别具有高度方法依赖性:CoNet、SparCC和RMT分别识别出224.00、44.00和19.00个关键类群,跨方法重叠率不足9.2%,表明算法选择显著影响关键节点判定。根瘤菌目(Rhizobiales)和酸杆菌目(Acidobacteriales)被所有方法一致识别为核心关键类群,证实其具有跨方法稳定性。环境因子相关性分析表明,共有关键类群与β-葡萄糖苷酶活性呈显著正相关,这不仅验证了其在纤维素降解中的核心生态功能,更揭示不同方法在关键生态过程解析上具有高度一致性。【结论】3种网络构建算法在解析根际微生物互作中呈现互补优势:CoNet适用于复杂竞争互作关系解析,SparCC在功能稳定性探究中具有更高可靠性,RMT则对核心功能模块挖掘表现出独特适用性;环境因子关联分析有效验证了关键类群的纤维素降解功能,且不同方法在核心生态过程解析中具有高度一致性。本研究为揭示植物-微生物互作机制及优化微生物网络研究方法提供了重要理论依据。
[Objective]We compared the rhizosphere microbial interaction network structure and keystone taxon identification arising from distinct network construction algorithms,aiming to clarify the characteristics and advantages of each algorithm in inferring microbial interactions and identifying keystone taxa,thereby providing a theoretical basis for methodological selection.[Methods]Taking the rhizosphere microbial community of Camphora migao(a rare plant)as the model system,we constructed molecular ecological networks with three mainstream algorithms:sparse correlations for compositional data(SparCC),random matrix theory(RMT),and co-occurrence network(CoNet).We comprehensively compared network structural features and keystone taxon identification across algorithms by integrating PICRUSt2 functional prediction with keystone taxa-environmental factor correlation analysis.[Results]Network construction algorithms significantly influenced the topological properties of networks.SparCC generated highly modular networks(relative modularity index,RM=1.31)with distinct interaction segregation(edge connectivity=0).RMT produced a single-module structure(RM=0.78)and homogeneous connectivity(closeness centralization index=0.22).Integration of 26.0%negative correlations in CoNet reduced modularity(RM=0.95),increased network diameter(33.22 steps),and decreased robustness.Keystone taxon identification was method-dependent.Specifically,CoNet,SparCC,and RMT identified 224.00,44.00,and 19.00 keystone taxa,respectively,with<9.2%cross-method overlap.Rhizobiales and Acidobacteriales were consistently identified as core keystone taxa by all methods,demonstrating cross-algorithm stability.The correlation analysis with environmental factors confirmed that these shared taxa significantly correlated withβ-glucosidase activity,validating their role in cellulose degradation and highlighting methodological consistency in identifying key ecological processes.[Conclusion]The three algorithms exhibited complementary strengths:CoNet resolved complex competitive interactions;SparCC reliably assessed functional stability;RMT uncovered core functional modules.The correlation analysis with environmental factors validated the cellulose degradation function of keystone taxa,with high cross-method consistency in core ecological process identification.Our work provides a theoretical foundation for elucidating plant-microbe interactions and optimizing microbial network construction.
作者
张靖怡
王悦云
陈敬忠
孙庆文
廖小锋
ZHANG Jingyi;WANG Yueyun;CHEN Jingzhong;SUN Qingwen;LIAO Xiaofeng(College of Pharmacy,Guizhou University of Traditional Chinese Medicine,Guiyang,Guizhou,China;Engineering Research Center for Conservation and Evaluation of Germplasm Resources of Traditional Chinese Medicine and Ethnic Medicinal Materials in Guizhou Province,Guiyang,Guizhou,China;Guizhou Key Laboratory for Raw Material of Traditional Chinese Medicine,Guiyang,Guizhou,China;Guizhou Botanical Garden,Guiyang,Guizhou,China)
出处
《微生物学报》
北大核心
2025年第12期5630-5649,共20页
Acta Microbiologica Sinica
基金
贵州省科技计划[黔科合基础MS(2025)176]
贵州省科技支撑计划(黔科合支撑[2023]一般049)
贵州省教育厅自然科学研究项目(黔教技[2024]12号)
贵州中医药大学博士启动基金(贵中医启动[2023]31号)。
关键词
微生物网络
米槁
网络理论
根际微生物
关键类群
microbial networks
Camphora migao
network theory
rhizosphere microorganisms
keystone taxa
